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As businesses continue to generate massive volumes of data, the problem is to store this data and efficiently use it to drive decision-making and innovation. Enterprise data management is critical for ensuring that data is effectively managed, integrated, and utilized throughout the organization.

One of the most recent developments in this field is the integration of Large Language Models (LLMs) with enterprise data lakes and warehouses.

This article will look at how orchestration frameworks help develop applications on enterprise data, with a focus on LLM integration, scalable data pipelines, and critical security and governance considerations. We will also give a case study on TechCorp, a company that has effectively implemented these technologies.


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LLM Integration with Enterprise Data Lakes and Warehouses

Large language models, like OpenAI’s GPT-4, have transformed natural language processing and comprehension. Integrating LLMs with company data lakes and warehouses allows for significant insights and sophisticated analytics capabilities.


Benefits of using orchestration frameworks - enterprise data management
Benefits of using orchestration frameworks


Here’s how orchestration frameworks help with this:

Streamlined Data Integration

Use orchestration frameworks like Apache Airflow and AWS Step Functions to automate ETL processes and efficiently integrate data from several sources into LLMs. This automation decreases the need for manual intervention and hence the possibility of errors.

Improved Data Accessibility

Integrating LLMs with data lakes (e.g., AWS Lake Formation, Azure Data Lake) and warehouses (e.g., Snowflake, Google BigQuery) allows enterprises to access a centralized repository for structured and unstructured data. This architecture allows LLMs to access a variety of datasets, enhancing their training and inference capabilities.

Real-time Analytics

Orchestration frameworks enable real-time data processing. Event-driven systems can activate LLM-based analytics as soon as new data arrives, enabling organizations to make quick decisions based on the latest information.


Explore 10 ways to generate more leads with data analytics


Scalable Data Pipelines for LLM Training and Inference

Creating and maintaining scalable data pipelines is essential for training and deploying LLMs in an enterprise setting.


enterprise data management - LLM Ops with orchestration frameworks
An example of integrating LLM Ops with orchestration frameworks – Source: LinkedIn


Here’s how orchestration frameworks work: 

Automated Workflows

Orchestration technologies help automate complex operations for LLM training and inference. Tools like Kubeflow Pipelines and Apache NiFi, for example, can handle the entire lifecycle, from data import to model deployment, ensuring that each step is completed correctly and at scale.

Resource Management

Effectively managing computing resources is crucial for processing vast amounts of data and complex computations in LLM procedures. Kubernetes, for example, can be combined with orchestration frameworks to dynamically assign resources based on workload, resulting in optimal performance and cost-effectiveness.

Monitoring and logging

Tracking data pipelines and model performance is essential for ensuring reliability. Orchestration frameworks include built-in monitoring and logging tools, allowing teams to identify and handle issues quickly. This guarantees that the LLMs produce accurate and consistent findings. 

Security and Governance Considerations for Enterprise LLM Deployments

Deploying LLMs in an enterprise context necessitates strict security and governance procedures to secure sensitive data and meet regulatory standards.


enterprise data management - policy-based orchestration framework
An example of a policy-based orchestration framework – Source: ResearchGate


Orchestration frameworks can meet these needs in a variety of ways:

  • Data Privacy and Compliance: Orchestration technologies automate data masking, encryption, and access control processes to implement privacy and compliance requirements, such as GDPR and CCPA. This guarantees that only authorized workers have access to sensitive information.
  • Audit Trails: Keeping accurate audit trails is crucial for tracking data history and changes. Orchestration frameworks can provide detailed audit trails, ensuring transparency and accountability in all data-related actions.
  • Access Control and Identity Management: Orchestration frameworks integrate with IAM systems to guarantee only authorized users have access to LLMs and data. This integration helps to prevent unauthorized access and potential data breaches.
  • Strong Security Protocols: Encryption at rest and in transport is essential for ensuring data integrity. Orchestration frameworks can automate the implementation of these security procedures, maintaining consistency across all data pipelines and operations.


How generative AI and LLMs work


Case Study: Implementing Orchestration Frameworks for Enterprise Data Management at TechCorp

TechCorp is a worldwide technology business focused on software solutions and cloud services. TechCorp generates and handles vast amounts of data every day for its global customer base. The corporation aimed to use its data to make better decisions, improve consumer experiences, and drive innovation.

To do this, TechCorp decided to connect Large Language Models (LLMs) with its enterprise data lakes and warehouses, leveraging orchestration frameworks to improve data management and analytics.  


TechCorp faced a number of issues in enterprise data management:  

  • Data Integration: Difficulty in creating a coherent view due to data silos from diverse sources.
  • Scalability: The organization required efficient data handling for LLM training and inference.
  • Security and Governance: Maintaining data privacy and regulatory compliance was crucial.  
  • Resource Management: Efficiently manage computing resources for LLM procedures without overpaying.




To address these difficulties, TechCorp designed an orchestration system built on Apache Airflow and Kubernetes. The solution included the following components:

Data Integration with Apache Airflow

  • ETL Pipelines were automated using Apache Airflow. Data from multiple sources (CRM systems, transactional databases, and log files) was extracted, processed, and fed into an AWS-based centralized data lake.
  • Data Harmonization: Airflow workflows harmonized data, making it acceptable for LLM training.

Scalable Infrastructure with Kubernetes

  • Dynamic Resource Allocation: Kubernetes used dynamic resource allocation to install LLMs and scale resources based on demand. This method ensured that computational resources were used efficiently during peak periods and scaled down when not required.
  • Containerization: LLMs and other services were containerized with Docker, allowing for consistent and stable deployment across several environments.
  • Data Encryption: All data at rest and in transit was encrypted. Airflow controlled the encryption keys and verified that data protection standards were followed.
  • Access Control: The integration with AWS Identity and Access Management (IAM) ensured that only authorized users could access sensitive data and LLM models.
  • Audit Logs: Airflow’s logging capabilities were used to create comprehensive audit trails, ensuring transparency and accountability for all data processes.


Read more about simplifying LLM apps with orchestration frameworks


LLM Integration and Deployment

  • Training Pipelines: Data pipelines for LLM training were automated with Airflow. The training data was processed and supplied into the LLM, which was deployed across Kubernetes clusters. 
  • Inference Services: Real-time inference services were established to process incoming data and deliver insights. These services were provided via REST APIs, allowing TechCorp applications to take advantage of the LLM’s capabilities.

Implementation Steps

  • Planning and design
    • Identifying major data sources and defining ETL needs.
    • Developed architecture for data pipelines, LLM integration, and Kubernetes deployments.
    • Implemented security and governance policies.
  • Deployment
    • Set up Apache Airflow to orchestrate data pipelines.
    • Set up Kubernetes clusters for scalability LLM deployment.
    • Implemented security measures like data encryption and IAM policies.
  • Testing and Optimization
    • Conducted thorough testing of ETL pipelines and LLM models.
    • Improved resource allocation and pipeline efficiency.
    • Monitored data governance policies continuously to ensure compliance.
  • Monitoring and maintenance
    • Implemented tools to track data pipeline and LLM performance.
    • Updated models and pipelines often to enhance accuracy with fresh data.
    • Conducted regular security evaluations and kept audit logs updated.




 TechCorp experienced substantial improvements in its data management and analytics capabilities:  

  • Improved Data Integration: A unified data perspective across the organization leads to enhanced decision-making.
  • Scalability: Efficient resource management and scalable infrastructure resulted in lower operational costs.  
  • Improved Security: Implemented strong security and governance mechanisms to maintain data privacy and regulatory compliance.
  • Advanced Analytics: Real-time insights from LLMs improved customer experiences and spurred innovation.


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Orchestration frameworks are critical for developing robust enterprise data management applications, particularly when incorporating sophisticated technologies such as Large Language Models.

These frameworks enable organizations to maximize the value of their data by automating complicated procedures, managing resources efficiently, and guaranteeing strict security and control.

TechCorp’s success demonstrates how leveraging orchestration frameworks may help firms improve their data management capabilities and remain competitive in a data-driven environment.


Written by Muhammad Hamza Naviwala

July 16, 2024

Generative AI applications like ChatGPT and Gemini are becoming indispensable in today’s world.

However, these powerful tools come with significant risks that need careful mitigation. Among these challenges is the potential for models to generate biased responses based on their training data or to produce harmful content, such as instructions on making a bomb.

Reinforcement Learning from Human Feedback (RLHF) has emerged as the industry’s leading technique to address these issues.

What is RLHF?

Reinforcement Learning from Human Feedback is a cutting-edge machine learning technique used to enhance the performance and reliability of AI models. By leveraging direct feedback from humans, RLHF aligns AI outputs with human values and expectations, ensuring that the generated content is both socially responsible and ethical.

Here are several reasons why RLHF is essential and its significance in AI development:

1. Enhancing AI Performance

  • Human-Centric Optimization: RLHF incorporates human feedback directly into the training process, allowing the model to perform tasks more aligned with human goals, wants, and needs. This ensures that the AI system is more accurate and relevant in its outputs.
  • Improved Accuracy: By integrating human feedback loops, RLHF significantly enhances model performance beyond its initial state, making the AI more adept at producing natural and contextually appropriate responses.


2. Addressing Subjectivity and Nuance

  • Complex Human Values: Human communication and preferences are subjective and context-dependent. Traditional methods struggle to capture qualities like creativity, helpfulness, and truthfulness. RLHF allows models to align better with these complex human values by leveraging direct human feedback.
  • Subjectivity Handling: Since human feedback can capture nuances and subjective assessments that are challenging to define algorithmically, RLHF is particularly effective for tasks that require a deep understanding of context and user intent.

3. Applications in Generative AI

  • Wide Range of Applications: RLHF is recognized as the industry standard technique for ensuring that large language models (LLMs) produce content that is truthful, harmless, and helpful. Applications include chatbots, image generation, music creation, and voice assistants .
  • User Satisfaction: For example, in natural language processing applications like chatbots, RLHF helps generate responses that are more engaging and satisfying to users by sounding more natural and providing appropriate contextual information.

4. Mitigating Limitations of Traditional Metrics

  • Beyond BLEU and ROUGE: Traditional metrics like BLEU and ROUGE focus on surface-level text similarities and often fail to capture the quality of text in terms of coherence, relevance, and readability. RLHF provides a more nuanced and effective way to evaluate and optimize model outputs based on human preferences.

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The Process of Reinforcement Learning from Human Feedback

Fine-tuning a model with Reinforcement Learning from Human Feedback involves a multi-step process designed to align the model with human preferences.

Reinforcement Learning from Human Feedback Process
Reinforcement Learning from Human Feedback Process

Step 1: Creating a Preference Dataset

A preference dataset is a collection of data that captures human preferences regarding the outputs generated by a language model.

This dataset is fundamental in the Reinforcement Learning from Human Feedback process, where it aligns the model’s behavior with human expectations and values.

Here’s a detailed explanation of what a preference dataset is and why it is created:

What is a Preference Dataset?

A preference dataset consists of pairs or sets of prompts and the corresponding responses generated by a language model, along with human annotations that rank these responses based on their quality or preferability.

Components of a Preference Dataset:

1. Prompts

Prompts are the initial queries or tasks posed to the language model. They serve as the starting point for generating responses.

These prompts are sampled from a predefined dataset and are designed to cover a wide range of scenarios and topics to ensure comprehensive training of the language model.


A prompt could be a question like “What is the capital of France?” or a more complex instruction such as “Write a short story about a brave knight”.


2. Generated Text Outputs

These are the responses generated by the language model when given a prompt.

The text outputs are the subject of evaluation and ranking by human annotators. They form the basis on which preferences are applied and learned.


For the prompt “What is the capital of France?”, the generated text output might be “The capital of France is Paris”.

3. Human Annotations

Human annotations involve the evaluation and ranking of the generated text outputs by human annotators.

Annotators compare different responses to the same prompt and rank them based on their quality or preferability. This helps in creating a more regularized and reliable dataset as opposed to direct scalar scoring, which can be noisy and uncalibrated.


Given two responses to the prompt “What is the capital of France?”, one saying “Paris” and another saying “Lyon,” annotators would rank “Paris” higher.

4. Preparing the Dataset:

Objective: Format the collected feedback for training the reward model.


  • Organize the feedback into a structured format, typically as pairs of outputs with corresponding preference labels.
  • This dataset will be used to teach the reward model to predict which outputs are more aligned with human preferences.

How generative AI and LLMs work

Step 2 – Training the Reward Model

Training the reward model is a pivotal step in the RLHF process, transforming human feedback into a quantitative signal that guides the learning of an AI system.

Below, we dive deeper into the key steps involved, including an introduction to model architecture selection, the training process, and validation and testing.

training the reward model for RLHF
Source: HuggingFace

1. Model Architecture Selection

Objective: Choose an appropriate neural network architecture for the reward model.


  • Select a Neural Network Architecture: The architecture should be capable of effectively learning from the feedback dataset, capturing the nuances of human preferences.
    • Feedforward Neural Networks: Simple and straightforward, these networks are suitable for basic tasks where the relationships in the data are not highly complex.
    • Transformers: These architectures, which power models like GPT-3, are particularly effective for handling sequential data and capturing long-range dependencies, making them ideal for language-related tasks.
  • Considerations: The choice of architecture depends on the complexity of the data, the computational resources available, and the specific requirements of the task. Transformers are often preferred for language models due to their superior performance in understanding context and generating coherent outputs.

2. Training the Reward Model

Objective: Train the reward model to predict human preferences accurately.


  • Input Preparation:
    • Pairs of Outputs: Use pairs of outputs generated by the language model, along with the preference labels provided by human evaluators.
    • Feature Representation: Convert these pairs into a suitable format that the neural network can process.
  • Supervised Learning:
    • Loss Function: Define a loss function that measures the difference between the predicted rewards and the actual human preferences. Common choices include mean squared error or cross-entropy loss, depending on the nature of the prediction task.
    • Optimization: Use optimization algorithms like stochastic gradient descent (SGD) or Adam to minimize the loss function. This involves adjusting the model’s parameters to improve its predictions.
  • Training Loop:
    • Forward Pass: Input the data into the neural network and compute the predicted rewards.
    • Backward Pass: Calculate the gradients of the loss function with respect to the model’s parameters and update the parameters accordingly.
    • Iteration: Repeat the forward and backward passes over multiple epochs until the model’s performance stabilizes.
  • Evaluation during Training: Monitor metrics such as training loss and accuracy to ensure the model is learning effectively and not overfitting the training data.

3. Validation and Testing

Objective: Ensure the reward model accurately predicts human preferences and generalizes well to new data.


  • Validation Set:
    • Separate Dataset: Use a separate validation set that was not used during training to evaluate the model’s performance.
    • Performance Metrics: Assess the model using metrics like accuracy, precision, recall, F1 score, and AUC-ROC to understand how well it predicts human preferences.
  • Testing:
    • Test Set: After validation, test the model on an unseen dataset to evaluate its generalization ability.
    • Real-world Scenarios: Simulate real-world scenarios to further validate the model’s predictions in practical applications.
  • Model Adjustment:
    • Hyperparameter Tuning: Adjust hyperparameters such as learning rate, batch size, and network architecture to improve performance.
    • Regularization: Apply techniques like dropout, weight decay, or data augmentation to prevent overfitting and enhance generalization.
  • Iterative Refinement:
    • Feedback Loop: Continuously refine the reward model by incorporating new human feedback and retraining the model.
    • Model Updates: Periodically update the reward model and re-evaluate its performance to maintain alignment with evolving human preferences.

By iteratively refining the reward model, AI systems can be better aligned with human values, leading to more desirable and acceptable outcomes in various applications.

Step 3 –  Fine-Tuning with Reinforcement Learning

Fine-tuning with RL is a sophisticated method used to enhance the performance of a pre-trained language model.

This method leverages human feedback and reinforcement learning techniques to optimize the model’s responses, making them more suitable for specific tasks or user interactions. The primary goal is to refine the model’s behavior to meet desired criteria, such as helpfulness, truthfulness, or creativity.

Finetuning with RL
Source: HuggingFace

Process of Fine-Tuning with Reinforcement Learning

  1. Reinforcement Learning Fine-Tuning:
    • Policy Gradient Algorithm: Use a policy-gradient RL algorithm, such as Proximal Policy Optimization (PPO), to fine-tune the language model. PPO is favored for its relative simplicity and effectiveness in handling large-scale models.
    • Policy Update: The language model’s parameters are adjusted to maximize the reward function, which combines the preference model’s output and a constraint on policy shift to prevent drastic changes. This ensures the model improves while maintaining coherence and stability.
      • Constraint on Policy Shift: Implement a penalty term, typically the Kullback–Leibler (KL) divergence, to ensure the updated policy does not deviate too far from the pre-trained model. This helps maintain the model’s original strengths while refining its outputs.
  2. Validation and Iteration:
    • Performance Evaluation: Evaluate the fine-tuned model using a separate validation set to ensure it generalizes well and meets the desired criteria. Metrics like accuracy, precision, and recall are used for assessment.
    • Iterative Updates: Continue iterating the process, using updated human feedback to refine the reward model and further fine-tune the language model. This iterative approach helps in continuously improving the model’s performance

Applications of RLHF

Reinforcement Learning from Human Feedback (RLHF) is essential for aligning AI systems with human values and enhancing their performance in various applications, including chatbots, image generation, music generation, and voice assistants.

1. Improving Chatbot Interactions

RLHF significantly improves chatbot tasks like summarization and question-answering. For summarization, human feedback on the quality of summaries helps train a reward model that guides the chatbot to produce more accurate and coherent outputs. In question-answering, feedback on the relevance and correctness of responses trains a reward model, leading to more precise and satisfactory interactions. Overall, RLHF enhances user satisfaction and trust in chatbots.

2. AI Image Generation

In AI image generation, RLHF enhances the quality and artistic value of generated images. Human feedback on visual appeal and relevance trains a reward model that predicts the desirability of new images. Fine-tuning the image generation model with reinforcement learning leads to more visually appealing and contextually appropriate images, benefiting digital art, marketing, and design.

3. Music Generation

RLHF improves the creativity and appeal of AI-generated music. Human feedback on harmony, melody, and enjoyment trains a reward model that predicts the quality of musical pieces. The music generation model is fine-tuned to produce compositions that resonate more closely with human tastes, enhancing applications in entertainment, therapy, and personalized music experiences.

4. Voice Assistants

Voice assistants benefit from RLHF by improving the naturalness and usefulness of their interactions. Human feedback on response quality and interaction tone trains a reward model that predicts user satisfaction. Fine-tuning the voice assistant ensures more accurate, contextually appropriate, and engaging responses, enhancing user experience in home automation, customer service, and accessibility support.

In Summary

RLHF is a powerful technique that enhances AI performance and user alignment across various applications. By leveraging human feedback to train reward models and using reinforcement learning for fine-tuning, RLHF ensures that AI-generated content is more accurate, relevant, and satisfying. This leads to more effective and enjoyable AI interactions in chatbots, image generation, music creation, and voice assistants.

July 4, 2024

There are predictions that applications of AI in healthcare could significantly reduce annual costs in the US by 2026. Estimates suggest reaching savings of around $150 billion.

This cost reduction is expected to come from a combination of factors, including:

  • Improved efficiency and automation of administrative tasks
  • More accurate diagnoses and treatment plans
  • Reduced hospital readmission rates

Large language models (LLMs) are transforming the landscape of medicine, bringing unprecedented changes to the way healthcare is delivered, managed, and even perceived.

These models, such as ChatGPT and GPT-4, are artificial intelligence (AI) systems trained on vast volumes of text data, enabling them to generate human-like responses and perform a variety of tasks with remarkable accuracy.

The impact of Artificial Intelligence (AI) in the field of medicine has been profound, transforming various aspects of healthcare delivery, management, and research.


blog banner - LLM bootamp


AI technologies, including machine learning, neural networks, and large language models (LLMs), have significantly contributed to improving the efficiency, accuracy, and quality of medical services.

Here’s an in-depth look at how AI is reshaping medicine and helping medical institutes enhance their operations:

Some Common Applications of LLMs in the Medical Profession

LLMs have been applied to numerous medical tasks, enhancing both clinical and administrative processes. Here are detailed examples:

AI in medicine


  • Diagnostic Assistance:

LLMs can analyze patient symptoms and medical history to suggest potential diagnoses. For instance, in a recent study, LLMs demonstrated the ability to answer medical examination questions and even assist in generating differential diagnoses. This capability can significantly reduce the burden on healthcare professionals by providing a second opinion and helping to identify less obvious conditions.

Moreover, AI algorithms can analyze complex medical data to aid in diagnosing diseases and predicting patient outcomes. This capability enhances the accuracy of diagnoses and helps in the early detection of conditions, which is crucial for effective treatment.

Further, AI systems like IBM Watson Health can analyze medical images to detect anomalies such as tumors or fractures with high precision. In some cases, these systems have demonstrated diagnostic accuracy comparable to or even surpassing that of experienced radiologists


Read more about: How AI in Healthcare has improved patient care


  • Clinical Documentation:

AI-powered clinical decision support systems (CDSS) provide healthcare professionals with evidence-based recommendations to optimize patient care. These systems analyze patient data, medical histories, and the latest research to suggest the most effective treatments.

In hospitals, CDSS can integrate with Electronic Health Records (EHR) to provide real-time alerts and treatment recommendations, reducing the likelihood of medical errors and ensuring adherence to clinical guidelines.

Another time-consuming task for physicians is documenting patient encounters. LLMs can automate this process by transcribing and summarizing clinical notes from doctor-patient interactions. This not only saves time but also ensures that records are more accurate and comprehensive.

  • Patient Interaction:

LLM chatbots like ChatGPT are being used to handle patient inquiries, provide health information, and even offer emotional support. These chatbots can operate 24/7, providing immediate responses and reducing the workload on human staff.

To further ease the doctor’s job, AI enables the customization of treatment plans based on individual patient data, including genetic information, lifestyle, and medical history. This personalized approach increases the effectiveness of treatments and reduces adverse effects.

AI algorithms can analyze a patient’s genetic profile to recommend personalized cancer treatment plans, selecting the most suitable drugs and dosages for the individual.

  • Research and Education:

LLMs assist in synthesizing vast amounts of medical literature, helping researchers stay up-to-date with the latest advancements. They can also generate educational content for both medical professionals and patients, ensuring that information dissemination is both quick and accurate.

The real-world implementation of LLMs in healthcare has shown promising results. For example, studies have demonstrated that LLMs can achieve diagnostic accuracy comparable to that of experienced clinicians in certain scenarios. In one study, LLMs improved the accuracy of clinical note classification, showing that these models could effectively handle vast amounts of medical data.


Your One-Stop Guide to Large Language Models and their Applications

Large Language Models Impacting Key Areas in Healthcare

By leveraging LLMs, medical professionals can save time, enhance their knowledge, and ultimately provide better care to their patients. This integration of AI into medical research and education highlights the transformative potential of technology in advancing healthcare.

Summarizing New Studies and Publications

Real-Time Information Processing

LLMs can rapidly process and summarize newly published medical research articles, clinical trial results, and medical guidelines. Given the vast amount of medical literature published every day, it is challenging for healthcare professionals to keep up. LLMs can scan through these documents, extracting key findings, methodologies, and conclusions, and present them in a concise format.

A medical researcher can use an LLM-powered tool to quickly review the latest papers on a specific topic like immunotherapy for cancer. Large language model applications like ChatGPT can provide summaries that highlight the most significant findings and trends, saving the researcher valuable time and ensuring they do not miss critical updates.

Continuous Learning Capability

Educational Content Generation

LLMs can generate educational materials, such as summaries of complex medical concepts, detailed explanations of new treatment protocols, and updates on recent advancements in various medical fields. This educational content can be tailored to different levels of expertise, from medical students to seasoned professionals.

Medical students preparing for exams can use an LLM-based application to generate summaries of textbooks and journal articles. Similarly, physicians looking to expand their knowledge in a new specialty can use the same tool to get up-to-date information and educational content.

Research Summarization and Analysis

A cardiologist wants to stay informed about the latest research on heart failure treatments. By using an LLM, the cardiologist receives daily or weekly summaries of new research articles, clinical trial results, and reviews. The LLM highlights the most relevant studies, allowing the cardiologist to quickly grasp new findings and incorporate them into practice.

Platforms like PubMed, integrated with LLMs, can provide personalized summaries and recommendations based on the cardiologist’s specific interests and past reading history.

How generative AI and LLMs work


Clinical Decision Support

A hospital integrates an LLM into its electronic health record (EHR) system to provide clinicians with real-time updates on best practices and treatment guidelines. When a clinician enters a diagnosis or treatment plan, the LLM cross-references the latest research and guidelines, offering suggestions or alerts if there are more recent or effective alternatives.

During the COVID-19 pandemic, LLMs were used to keep healthcare providers updated on rapidly evolving treatment protocols and research findings, ensuring that the care provided was based on the most current and accurate information available.

Personalized Learning for Healthcare Professionals

An online medical education platform uses LLMs to create personalized learning paths for healthcare professionals. Based on their previous learning history, specialties, and interests, the platform curates the most relevant courses, articles, and case studies, ensuring continuous professional development.

Platforms like Coursera or Udemy can leverage LLMs to recommend personalized courses and materials to doctors looking to earn continuing medical education (CME) credits in their respective fields.

Enhanced Efficiency and Accuracy

LLMs can process and analyze medical data faster than humans, leading to quicker diagnosis and treatment plans. This increased efficiency can lead to better patient outcomes and higher satisfaction rates. Furthermore, the accuracy of AI in healthcare tasks such as diagnostic assistance and clinical documentation ensures that healthcare providers can trust the recommendations and insights generated by these models.

Cost Reduction

By automating routine tasks, large language models can significantly reduce operational costs for hospitals and medical companies. This allows healthcare providers to allocate resources more effectively, focusing human expertise on more complex cases that require personalized attention.

Improved Patient Engagement

LLM-driven chatbots and virtual assistants can engage with patients more effectively, answering their questions, providing timely information, and offering support. This continuous engagement can lead to better patient adherence to treatment plans and overall improved health outcomes.

Facilitating Research and Continuous Learning

LLMs can help medical professionals stay abreast of the latest research by summarizing new studies and publications. This continuous learning capability ensures that healthcare providers are always informed about the latest advancements and best practices in medicine.



Future of AI in Healthcare

Large language model applications are revolutionizing the medical profession by enhancing efficiency, accuracy, and patient engagement. As these models continue to evolve, their integration into healthcare systems promises to unlock new levels of innovation and improvement in patient care.

The integration of AI into healthcare systems promises to unlock new levels of innovation and efficiency, ultimately leading to better patient outcomes and a more effective healthcare delivery system.


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June 21, 2024

We have all been using the infamous ChatGPT for quite a while. But the thought of our data being used to train models has made most of us quite uneasy.

People are willing to use on-device AI applications as opposed to cloud-based applications for the obvious reasons of privacy.

Deploying an LLM application on edge devices—such as smartphones, IoT devices, and embedded systems—can provide significant benefits, including reduced latency, enhanced privacy, and offline capabilities.

In this blog, we will explore the process of deploying an LLM application on edge devices, covering everything from model optimization to practical implementation steps.

Understanding Edge Devices

Edge devices are hardware devices that perform data processing at the location where data is generated. Examples include smartphones, IoT devices, and embedded systems.

Edge computing offers several advantages over cloud computing, such as reduced latency, enhanced privacy, and the ability to operate offline.

However, deploying applications on edge devices has challenges, including limited computational resources and power constraints.

Preparing for On-Device AI Deployment

Before deploying an on-device AI application, several considerations must be addressed:

  • Application Use Case and Requirements: Understand the specific use case for the LLM application and its performance requirements. This helps in selecting the appropriate model and optimization techniques.
  • Data Privacy and Security: Ensure the deployment complies with data privacy and security regulations, particularly when processing sensitive information on edge devices.
a roadmap to deploy on-device AI
a roadmap to deploy on-device AI

Choosing the Right Language Model

Selecting the right language model for edge deployment involves balancing performance and resource constraints. Here are key factors to consider:

  • Model Size and Complexity:

    Smaller models are generally more suitable for edge devices. These devices have limited computational capacity, so a lighter model ensures smoother operation. Opt for models that strike a balance between size and performance, making them efficient without sacrificing too much accuracy.
  • Performance Requirements:

    Your chosen model must meet the application’s accuracy and responsiveness needs.

    This means it should be capable of delivering precise results quickly.

    While edge devices might not handle the heaviest models, ensure the selected LLM is efficient enough to run effectively on the target device. Prioritize models that are optimized for speed and resource usage without compromising the quality of output.

    In summary, the right language model for on-device AI deployment should be compact yet powerful, and tailored to the specific performance demands of your application. Balancing these factors is key to a successful deployment.

Model Optimization Techniques

Optimizing Large Language Models is crucial for efficient edge deployment. Here are several key techniques to achieve this:

LLM Optimization Techniques for On-Device AI Deployment
LLM Optimization Techniques for On-Device AI Deployment

1. Quantization

Quantization reduces the precision of the model’s weights. By using lower precision (e.g., converting 32-bit floats to 8-bit integers), memory usage and computation requirements decrease significantly. This reduction leads to faster inference and lower power consumption, making quantization a popular technique for deploying LLMs on edge devices.

2. Pruning

Pruning involves removing redundant or less important neurons and connections within the model. By eliminating these parts, the model’s size is reduced, leading to faster inference times and lower resource consumption. Pruning helps maintain model performance while making it more efficient and manageable for edge deployment.


3. Knowledge Distillation

Knowledge distillation is a technique where a smaller model (the student) is trained to mimic the behavior of a larger, more complex model (the teacher). The student model learns to reproduce the outputs of the teacher model, retaining much of the original accuracy while being more efficient. This approach allows for deploying a compact, high-performing model on edge devices.

4. Low-Rank Adaptation (LoRA) and QLoRA

Low-Rank Adaptation (LoRA) and its variant QLoRA are techniques designed to adapt and compress models while maintaining performance. LoRA involves factorizing the weight matrices of the model into lower-dimensional matrices, reducing the number of parameters without significantly affecting accuracy. QLoRA further quantizes these lower-dimensional matrices, enhancing efficiency. These methods enable the deployment of robust models on resource-constrained edge devices.

5. Hardware and Software Requirements

Deploying on-device AI necessitates specific hardware and software capabilities to ensure smooth and efficient operation. Here’s what you need to consider:

Hardware Requirements

To run on-device AI applications smoothly, you need to ensure the hardware meets certain criteria:

  • Computational Power: The device should have a powerful processor, ideally with multiple cores, to handle the demands of LLM inference. Devices with specialized AI accelerators, such as GPUs or NPUs, are highly beneficial.
  • Memory: Adequate RAM is crucial as LLMs require significant memory for loading and processing data. Devices with limited RAM might struggle to run larger models.
  • Storage: Sufficient storage capacity is needed to store the model and any related data. Flash storage or SSDs are preferable for faster read/write speeds.

Software Tools and Frameworks

The right software tools and frameworks are essential for deploying on-device AI. These tools facilitate model optimization, deployment, and inference. Key tools and frameworks include:

  • TensorFlow Lite: A lightweight version of TensorFlow designed for mobile and edge devices. It optimizes models for size and latency, making them suitable for resource-constrained environments.
  • ONNX Runtime: An open-source runtime that allows models trained in various frameworks to be run efficiently on multiple platforms. It supports a wide range of optimizations to enhance performance on edge devices.
  • PyTorch Mobile: A version of PyTorch tailored for mobile and embedded devices. It provides tools to optimize and deploy models, ensuring they run efficiently on the edge.
  • Edge AI SDKs: Many hardware manufacturers offer specialized SDKs for deploying AI models on their devices. These SDKs are optimized for the hardware and provide additional tools for model deployment and management.

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Deployment Strategies for LLM Application

Deploying Large Language Models on edge devices presents unique challenges and opportunities from an AI engineer’s perspective. Effective deployment strategies are critical to ensure optimal performance, resource management, and user experience.

Here, we delve into three primary strategies: On-Device Inference, Hybrid Inference, and Model Partitioning.

On-Device Inference

On-device inference involves running the entire LLM directly on the edge device. This approach offers several significant advantages, particularly in terms of latency, privacy, and offline capability of the LLM application.


  • Low Latency: On-device inference minimizes response time by eliminating the need to send data to and from a remote server. This is crucial for real-time applications such as voice assistants and interactive user interfaces.
  • Offline Capability: By running the model locally, applications can function without an internet connection. This is vital for use cases in remote areas or where connectivity is unreliable.
  • Enhanced Privacy: Keeping data processing on-device reduces the risk of data exposure during transmission. This is particularly important for sensitive applications, such as healthcare or financial services.


  • Resource Constraints: Edge devices typically have limited computational power, memory, and storage compared to cloud servers. Engineers must optimize models to fit within these constraints without significantly compromising performance.
  • Power Consumption: Intensive computations can drain battery life quickly, especially in portable devices. Balancing performance with energy efficiency is crucial.

Implementation Considerations:

  • Model Optimization: Techniques such as quantization, pruning, and knowledge distillation are essential to reduce the model’s size and computational requirements.
  • Efficient Inference Engines: Utilizing frameworks like TensorFlow Lite or PyTorch Mobile, which are optimized for mobile and embedded devices, can significantly enhance performance.

Hybrid Inference

Hybrid inference leverages both edge and cloud resources to balance performance and resource constraints. This strategy involves running part of the model on the edge device and part on the cloud server.


  • Balanced Load: By offloading resource-intensive computations to the cloud, hybrid inference reduces the burden on the edge device, enabling the deployment of more complex models.
  • Scalability: Cloud resources can be scaled dynamically based on demand, providing flexibility and robustness for varying workloads.
  • Reduced Latency for Critical Tasks: Immediate, latency-sensitive tasks can be processed locally, while more complex processing can be handled by the cloud.


  • Network Dependency: The performance of hybrid inference is contingent on the quality and reliability of the network connection. Network latency or interruptions can impact the user experience.
  • Data Privacy: Transmitting data to the cloud poses privacy risks. Ensuring secure data transmission and storage is paramount.

Implementation Considerations:

  • Model Segmentation: Engineers need to strategically segment the model, determining which parts should run on the edge and which on the cloud.
  • Efficient Data Handling: Minimize the amount of data transferred between the edge and cloud to reduce latency and bandwidth usage. Techniques such as data compression and smart caching can be beneficial.
  • Robust Fallbacks: Implement fallback mechanisms to handle network failures gracefully, ensuring the application remains functional even when connectivity is lost.

Model Partitioning

Model partitioning involves splitting the LLM into smaller, manageable segments that can be distributed across multiple devices or environments. This approach can enhance efficiency and scalability.


  • Distributed Computation: By distributing the model across different devices, the computational load is balanced, making it feasible to run more complex models on resource-constrained edge devices.
  • Flexibility: Different segments of the model can be optimized independently, allowing for tailored optimizations based on the capabilities of each device.
  • Scalability: Model partitioning facilitates scalability, enabling the deployment of large models across diverse hardware configurations.


  • Complex Implementation: Partitioning a model requires careful planning and engineering to ensure seamless integration and communication between segments.
  • Latency Overhead: Communication between different model segments can introduce latency. Engineers must optimize inter-segment communication to minimize this overhead.
  • Consistency: Ensuring consistency and synchronization between model segments is critical to maintaining the overall model’s performance and accuracy.

Implementation Considerations:

  • Segmentation Strategy: Identify logical points in the model where it can be partitioned without significant loss of performance. This might involve separating different layers or components based on their computational requirements.
  • Communication Protocols: Use efficient communication protocols to minimize latency and ensure reliable data transfer between model segments.
  • Resource Allocation: Optimize resource allocation for each device based on its capabilities, ensuring that each segment runs efficiently.

How generative AI and LLMs work

Implementation Steps

Here’s a step-by-step guide to deploying an on-device AI application:

  1. Preparing the Development Environment: Set up the necessary tools and frameworks for development.
  2. Optimizing the Model: Apply optimization techniques to make the model suitable for edge deployment.
  3. Integrating with Edge Device Software: Ensure the model can interact with the device’s software and hardware.
  4. Testing and Validation: Thoroughly test the model on the edge device to ensure it meets performance and accuracy requirements.
  5. Deployment and Monitoring: Deploy the model to the edge device and monitor its performance, making adjustments as needed.

Future of On-Device AI Applications

Deploying on-device AI applications can significantly enhance user experience by providing fast, efficient, and private AI-powered functionalities. By understanding the challenges and leveraging optimization techniques and deployment strategies, developers can successfully implement on-device AI.

June 20, 2024

Imagine effortlessly asking your business intelligence dashboard any question and receiving instant, insightful answers. This is not a futuristic concept but a reality unfolding through the power of Large Language Models (LLMs).

Descriptive analytics is at the core of this transformation, turning raw data into comprehensible narratives. When combined with the advanced capabilities of LLMs, Business Intelligence (BI) dashboards evolve from static displays of numbers into dynamic tools that drive strategic decision-making. 

LLMs are changing the way we interact with data. These advanced AI models excel in natural language processing (NLP) and understanding, making them invaluable for enhancing descriptive analytics in Business Intelligence (BI) dashboards.


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In this blog, we will explore the power of LLMs in enhancing descriptive analytics and its impact of business intelligence dashboards.

Understanding Descriptive Analytics

Descriptive analytics is the most basic and common type of analytics that focuses on describing, summarizing, and interpreting historical data.

Companies use descriptive analytics to summarize and highlight patterns in current and historical data, enabling them to make sense of vast amounts of raw data to answer the question, “What happened?” through data aggregation and data visualization techniques.

The Evolution of Dashboards: From Static to LLM

Initially, the dashboards served as simplified visual aids, offering a basic overview of key metrics amidst cumbersome and text-heavy reports.

However, as businesses began to demand real-time insights and more nuanced data analysis, the static nature of these dashboards became a limiting factor forcing them to evolve into dynamic, interactive tools. The dashboards transformed into Self-service BI tools with drag-drop functionalities and increased focus on interactive user-friendly visualization.

This is not it, with the realization of increasing data, Business Intelligence (BI) dashboards shifted to cloud-based mobile platforms, facilitating integration to various data sources, and allowing remote collaboration. Finally, the Business Intelligence (BI) dashboard integration with LLMs has unlocked the wonderful potential of analytics.


Explore the Top 5 Marketing Analytics Tools for Success


Role of Descriptive Analytics in Business Intelligence Dashboards and its Limitations

Despite of these shifts, the analysis of dashboards before LLMs remained limited in its ability to provide contextual insights and advanced data interpretations, offering a retrospective view of business performance without predictive or prescriptive capabilities. 

The following are the basic capabilities of descriptive analytics:

Defining Visualization

Descriptive analytics explains visualizations like charts, graphs, and tables, helping users quickly grasp key insights. However, this requires manually describing the analyzed insights derived from SQL queries, requiring analytics expertise and knowledge of SQL. 

Trend Analysis

By identifying patterns over time, descriptive analytics helps businesses understand historical performance and predict future trends, making it critical for strategic planning and decision-making.

However, traditional analysis of Business Intelligence (BI) dashboards may struggle to identify intricate patterns within vast datasets, providing inaccurate results that can critically impact business decisions. 


Reports developed through descriptive analytics summarize business performance. These reports are essential for documenting and communicating insights across the organization.

However, extracting insights from dashboards and presenting them in an understandable format can take time and is prone to human error, particularly when dealing with large volumes of data.


How generative AI and LLMs work


LLMs: A Game-Changer for Business Intelligence Dashboards

Advanced Query Handling 

Imagine you would want to know “What were the top-selling products last quarter?” Conventionally, data analysts would write an SQL query, or create a report in a Business Intelligence (BI) tool to find the answer. Wouldn’t it be easier to ask those questions in natural language?  

LLMs enable users to interact with dashboards using natural language queries. This innovation acts as a bridge between natural language and complex SQL queries, enabling users to engage in a dialogue, ask follow-up questions, and delve deeper into specific aspects of the data.

Improved Visualization Descriptions

Advanced Business Intelligence (BI) tools integrated with LLMs offer natural language interaction and automatic summarization of key findings. They can automatically generate narrative summaries, identify trends, and answer questions for complex data sets, offering a comprehensive view of business operations and trends without any hustle and minimal effort.

Predictive Insights

With the integration of a domain-specific Large Language Model (LLM), dashboard analysis can be expanded to offer predictive insights enabling organizations to leverage data-driven decision-making, optimize outcomes, and gain a competitive edge.

Dashboards supported by Large Language Mode (LLMs) utilize historical data and statistical methods to forecast future events. Hence, descriptive analytics goes beyond “what happened” to “what happens next.”

Prescriptive Insights

Beyond prediction, descriptive analytics powered by LLMs can also offer prescriptive recommendations, moving from “what happens next” to “what to do next.” By considering numerous factors, preferences, and constraints, LLMs can recommend optimal actions to achieve desired outcomes. 


Read more about Data Visualization


Example – Power BI

The Copilot integration in Power BI offers advanced Business Intelligence (BI) capabilities, allowing you to ask Copilot for summaries, insights, and questions about visuals in natural language. Power BI has truly paved the way for unparalleled data discovery from uncovering insights to highlighting key metrics with the power of Generative AI.

Here is how you can get started using Power BI with Copilot integration;

Step 1

Open Power BI. Create workspace (To use Copilot, you need to select a workspace that uses a Power BI Premium per capacity, or a paid Microsoft Fabric capacity).

Step 2

Upload your business data from various sources. You may need to clean and transform your data as well to gain better insights. For example, a sample ‘sales data for hotels and resorts’ is used here.


Uploading data - business intelligence dashboards
Uploading data


Step 3

Use Copilot to unleash the potential insights of your data. 

Start by creating reports in the Power BI service/Desktop. Copilot allows the creation of insightful reports for descriptive analytics by just using the requirements that you can provide in natural language.  

For example: Here a report is created by using the following prompt:


report creation prompt using Microsoft Copilot - business intelligence dashboards
An example of a report creation prompt using Microsoft Copilot – Source: Copilot in Power BI Demo


Copilot has created a report for the customer profile that includes the requested charts and slicers and is also fully interactive, providing options to conveniently adjust the outputs as needed. 


Power BI report created using Microsoft Copilot - business intelligence dashboards
An example of a Power BI report created using Microsoft Copilot – Source: Copilot in Power BI Demo


Not only this, but you can also ask analysis questions about the reports as explained below.


asking analysis question from Microsoft Copilot - business intelligence dashboards
An example of asking analysis question from Microsoft Copilot – Source: Copilot in Power BI Demo


The copilot now responds by adding a new page to the report. It explains the ‘main drivers for repeat customer visits’ by using advanced analysis capabilities to find key influencers for variables in the data. As a result, it can be seen that the ‘Purchased Spa’ service has the biggest influence on customer returns followed ‘Rented Sports Equipment’ service.


example of asking analysis question from Microsoft Copilot - business intelligence dashboards
An example of asking analysis questions from Microsoft Copilot – Source: Copilot in Power BI Demo


Moreover, you can ask to include, exclude, or summarize any visuals or pages in the generated reports. Other than generating reports, you can even refer to your existing dashboard to question or summarize the insights or to quickly create a narrative for any part of the report using Copilot. 

Below you can see how the Copilot has generated a fully dynamic narrative summary for the report, highlighting the useful insights from data along with proper citation from where within the report the data was taken.


narrative generation by Microsoft PowerBI Copilot - business intelligence dashboards
An example of narrative generation by Microsoft Power BI Copilot – Source: Copilot in Power BI Demo


Microsoft Copilot simplifies Data Analysis Expressions (DAX) formulas by generating and editing these complex formulas. In Power BI, you can easily navigate to the ‘Quick Measure’ button in the calculations section of the Home tab. (if you do not see ‘suggestions with Copilot,’ then you may enable it from settings.

Otherwise, you may need to get it enabled by your Power BI Administrator).

Quick measures are predefined measures, eliminating the need for creating your own DAX syntax. It’s generated automatically according to the input you provide in Natural Language via the dialog box. They execute a series of DAX commands in the background and display the outcomes for utilization in your report.


Quick Measure – Suggestions with Copilot - business intelligence dashboards
Quick Measure – Suggestions with Copilot


In the below example, it can be seen that the copilot gives suggestion for a quick measure based on the data, generating the DAX formula as well. If you find the suggested measure satisfactory, you can simply click the “Add” button to seamlessly incorporate it into your model.


DAX generation using Quick Measure - business intelligence dashboards
An example of DAX generation using Quick Measure – Source: Microsoft Learn


There can be several other things that you can do with copilot with clear and understandable prompts to questions about your data and generate more insightful reports for your Business Intelligence (BI) dashboards.  

Hence, we can say that Power BI with Copilot has proven to be the transformative force in the landscape of data analytics, reshaping how businesses leverage their data’s potential.


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Embracing the LLM-led Era in Business Intelligence

Descriptive analytics is fundamental to Business Intelligence (BI) dashboards, providing essential insights through data aggregation, visualization, trend analysis, and reporting. 

The integration of Large Language Models enhances these capabilities by enabling advanced query handling, improving visualization descriptions, and reporting, and offering predictive and prescriptive insights.

This new LLM-led era in Business Intelligence (BI) is transforming the dynamic landscape of data analytics, offering a glimpse into a future where data-driven insights empower organizations to make informed decisions and gain a competitive edge.

June 17, 2024

Data scientists are continuously advancing with AI tools and technologies to enhance their capabilities and drive innovation in 2024. The integration of AI into data science has revolutionized the way data is analyzed, interpreted, and utilized.

Data science education should incorporate practical exercises and projects that involve using LLML platforms. By providing hands-on experience, students can gain a deeper understanding of how to leverage these platforms effectively. This can include tasks such as data preprocessing, model selection, and hyperparameter tuning using LLML tools.


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Here are some key ways data scientists are leveraging AI tools and technologies:

6 Ways Data Scientists are Leveraging Large Language Models with Examples

Advanced Machine Learning Algorithms:

Data scientists are utilizing more advanced machine learning algorithms to derive valuable insights from complex and large datasets. These algorithms enable them to build more accurate predictive models, identify patterns, and make data-driven decisions with greater confidence.

Think of Netflix and how it recommends movies and shows you might like based on what you’ve watched before. Data scientists are using more advanced machine learning algorithms to do similar things in various industries, like predicting customer behavior or optimizing supply chain operations.


Here’s your guide to Machine Learning Model Deployment


Automated Feature Engineering:

AI tools are being used to automate the process of feature engineering, allowing data scientists to extract, select, and transform features in a more efficient and effective manner. This automation accelerates the model development process and improves the overall quality of the models.

Imagine if you’re on Amazon and it suggests products that are related to what you’ve recently viewed or bought. This is powered by automated feature engineering, where AI helps identify patterns and relationships between different products to make these suggestions more accurate.

Natural Language Processing (NLP):

Data scientists are incorporating NLP techniques and technologies to analyze and derive insights from unstructured data such as text, audio, and video. This enables them to extract valuable information from diverse sources and enhance the depth of their analysis.

Have you used voice assistants like Siri or Alexa? Data scientists are using NLP to make these assistants smarter and more helpful. They’re also using NLP to analyze customer feedback and social media posts to understand sentiment and improve products and services.

Enhanced Data Visualization:

AI-powered data visualization tools are enabling data scientists to create interactive and dynamic visualizations that facilitate better communication of insights and findings. These tools help in presenting complex data in a more understandable and compelling manner.

When you see interactive and colorful charts on news websites or in business presentations that help explain complex data, that’s the power of AI-powered data visualization tools. Data scientists are using these tools to make data more understandable and actionable.

Real-time Data Analysis:

With AI-powered technologies, data scientists can perform real-time data analysis, allowing businesses to make immediate decisions based on the most current information available. This capability is crucial for industries that require swift and accurate responses to changing conditions.

In industries like finance and healthcare, real-time data analysis is crucial. For example, in finance, AI helps detect fraudulent transactions in real-time, while in healthcare, it aids in monitoring patient vitals and alerting medical staff to potential issues.

Autonomous Model Deployment:

AI tools are streamlining the process of deploying machine learning models into production environments. Data scientists can now leverage automated model deployment solutions to ensure seamless integration and operation of their predictive models.

Data scientists are using AI to streamline the deployment of machine learning models into production environments. Just like how self-driving cars operate autonomously, AI tools are helping models to be deployed seamlessly and efficiently.

As data scientists continue to embrace and integrate AI tools and technologies into their workflows, they are poised to unlock new possibilities in data analysis, decision-making, and business optimization in 2024 and beyond.


Read more: Your One-Stop Guide to Large Language Models and their Applications

Usage of Generative AI Tools like ChatGPT for Data Scientists

GPT (Generative Pre-trained Transformer) and similar natural language processing (NLP) models can be incredibly useful for data scientists in various tasks. Here are some ways data scientists can leverage GPT for regular data science tasks with real-life examples

  • Text Generation and Summarization: Data scientists can use GPT to generate synthetic text or create automatic summaries of lengthy documents. For example, in customer feedback analysis, GPT can be used to summarize large volumes of customer reviews to identify common themes and sentiments.


  • Language Translation: GPT can assist in translating text from one language to another, which can be beneficial when dealing with multilingual datasets. For instance, in a global marketing analysis, GPT can help translate customer feedback from different regions to understand regional preferences and sentiments.


  • Question Answering: GPT can be employed to build question-answering systems that can extract relevant information from unstructured text data. In a healthcare setting, GPT can support the development of systems that extract answers from medical literature to aid in diagnosis and treatment decisions.


  • Sentiment Analysis: Data scientists can utilize GPT to perform sentiment analysis on social media posts, customer feedback, or product reviews to gauge public opinion. For example, in brand reputation management, GPT can help identify and analyze sentiments expressed in online discussions about a company’s products or services.


  • Data Preprocessing and Labeling: GPT can be used for automated data preprocessing tasks such as cleaning and standardizing textual data. In a research context, GPT can assist in automatically labeling research papers based on their content, making them easier to categorize and analyze.


By incorporating GPT into their workflows, data scientists can enhance their ability to extract valuable insights from unstructured data, automate repetitive tasks, and improve the efficiency and accuracy of their analyses.


Also explore these 6 Books to Learn Data Science


AI Tools for Data Scientists

In the realm of AI tools for data scientists, there are several impactful ones that are driving significant advancements in the field. Let’s explore a few of these tools and their applications with real-life examples:

  • TensorFlow:

– TensorFlow is an open-source machine learning framework developed by Google. It is widely used for building and training machine learning models, particularly neural networks.

– Example: Data scientists can utilize TensorFlow to develop and train deep learning models for image recognition tasks. For instance, in the healthcare industry, TensorFlow can be employed to analyze medical images for the early detection of diseases such as cancer.

  • PyTorch:

– PyTorch is another popular open-source machine learning library, particularly favored for its flexibility and ease of use in building and training neural networks.

– Example: Data scientists can leverage PyTorch to create and train natural language processing (NLP) models for sentiment analysis of customer reviews. This can help businesses gauge public opinion about their products and services.

  • Scikit-learn:

– Scikit-learn is a versatile machine-learning library that provides simple and efficient tools for data mining and data analysis.

– Example: Data scientists can use Scikit-learn for clustering customer data to identify distinct customer segments based on their purchasing behavior. This can inform targeted marketing strategies and personalized recommendations.


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– offers an open-source platform for scalable machine learning and deep learning. It provides tools for building and deploying machine learning models.

– Example: Data scientists can employ to develop predictive models for demand forecasting in retail, helping businesses optimize their inventory and supply chain management.

  • GPT-3 (Generative Pre-trained Transformer 3):

– GPT-3 is a powerful natural language processing model developed by OpenAI, capable of generating human-like text and understanding and responding to natural language queries.

– Example: Data scientists can utilize GPT-3 for generating synthetic text or summarizing large volumes of customer feedback to identify common themes and sentiments, aiding in customer sentiment analysis and product improvement.

These AI tools are instrumental in enabling data scientists to tackle a wide range of tasks, from image recognition and natural language processing to predictive modeling and recommendation systems, driving innovation and insights across various industries.


Read more: 6 Python Libraries for Data Science


Relevance of Data Scientists in the Era of Large Language Models

With the advent of Low-Code Machine Learning (LLML) platforms, data science education can stay relevant by adapting to the changing landscape of the industry. Here are a few ways data science education can evolve to incorporate LLML:

  • Emphasize Core Concepts: While LLML platforms provide pre-built solutions and automated processes, it’s essential for data science education to focus on teaching core concepts and fundamentals. This includes statistical analysis, data preprocessing, feature engineering, and model evaluation. By understanding these concepts, data scientists can effectively leverage the LLML platforms to their advantage.
  • Teach Interpretation and Validation: LLML platforms often provide ready-to-use models and algorithms. However, it’s crucial for data science education to teach students how to interpret and validate the results generated by these platforms. This involves understanding the limitations of the models, assessing the quality of the data, and ensuring the validity of the conclusions drawn from LLML-generated outputs.


How generative AI and LLMs work


  • Foster Critical Thinking: LLML platforms simplify the process of building and deploying machine learning models. However, data scientists still need to think critically about the problem at hand, select appropriate algorithms, and interpret the results. Data science education should encourage critical thinking skills and teach students how to make informed decisions when using LLML platforms.
  • Stay Up-to-Date: LLML platforms are constantly evolving, introducing new features and capabilities. Data science education should stay up-to-date with these advancements and incorporate them into the curriculum. This can be done through partnerships with LLML platform providers, collaboration with industry professionals, and continuous monitoring of the latest trends in the field.

By adapting to the rise of LLML platforms, data science education can ensure that students are equipped with the necessary skills to leverage these tools effectively. It’s important to strike a balance between teaching core concepts and providing hands-on experience with LLML platforms, ultimately preparing students to navigate the evolving landscape of data science.

June 10, 2024

Time series data, a continuous stream of measurements captured over time, is the lifeblood of countless fields. From stock market trends to weather patterns, it holds the key to understanding and predicting the future.

Traditionally, unraveling these insights required wading through complex statistical analysis and code. However, a new wave of technology is making waves: Large Language Models (LLMs) are revolutionizing how we analyze time series data, especially with the use of LangChain agents.

In this article, we will navigate the exciting world of LLM-based time series analysis. We will explore how LLMs can be used to unearth hidden patterns in your data, forecast future trends, and answer your most pressing questions about time series data using plain English.


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We will see how to integrate Langchain’s Pandas Agent, a powerful LLM tool, into your existing workflow for seamless exploration. 

Uncover Hidden Trends with LLMs 

LLMs are powerful AI models trained on massive amounts of text data. They excel at understanding and generating human language. But their capabilities extend far beyond just words. Researchers are now unlocking their potential for time series analysis by bridging the gap between numerical data and natural language. 

Here’s how LLMs are transforming the game: 

  • Natural Language Prompts: Imagine asking questions about your data like, “Is there a correlation between ice cream sales and temperature?” LLMs can be prompted in natural language, deciphering your intent, and performing the necessary analysis on the underlying time series data. 
  • Pattern Recognition: LLMs excel at identifying patterns in language. This ability translates to time series data as well. They can uncover hidden trends, periodicities, and seasonality within the data stream. 
  • Uncertainty Quantification: Forecasting the future is inherently uncertain. LLMs can go beyond just providing point predictions. They can estimate the likelihood of different outcomes, giving you a more holistic picture of potential future scenarios.

LLM Applications Across Various Industries 

While LLM-based time series analysis is still evolving, it holds immense potential for various applications: 

  • Financial analysis: Analyze market trends, predict stock prices, and identify potential risks with greater accuracy. 
  • Supply chain management: Forecast demand fluctuations, optimize inventory levels, and prevent stockouts. 
  • Scientific discovery: Uncover hidden patterns in environmental data, predict weather patterns, and accelerate scientific research. 
  • Anomaly detection: Identify unusual spikes or dips in data streams, pinpointing potential equipment failures or fraudulent activities. 


How generative AI and LLMs work


LangChain Pandas Agent 

Lang Chain Pandas Agent is a Python library built on top of the popular Pandas library. It provides a comprehensive set of tools and functions specifically designed for data analysis. The agent simplifies the process of handling, manipulating, and visualizing time series data, making it an ideal choice for both beginners and experienced data analysts. 

It exemplifies the power of LLMs for time series analysis. It acts as a bridge between these powerful language models and the widely used Panda’s library for data manipulation. Users can interact with their data using natural language commands, making complex analysis accessible to a wider audience. 

Key Features 

  • Data Preprocessing: The agent offers various techniques for cleaning and preprocessing time series data, including handling missing values, removing outliers, and normalizing data. 
  • Time-based Indexing: Lang Chain Pandas Agent allows users to easily set time-based indexes, enabling efficient slicing, filtering, and grouping of time series data. 
  • Resampling and Aggregation: The agent provides functions for resampling time series data at different frequencies and aggregating data over specific time intervals. 
  • Visualization: With built-in plotting capabilities, the agent allows users to create insightful visualizations such as line plots, scatter plots, and histograms to analyze time series data. 
  • Statistical Analysis: Lang Chain Pandas Agent offers a wide range of statistical functions to calculate various metrics like mean, median, standard deviation, and more.


Read along to understand sentiment analysis in LLMs


Time Series Analysis with LangChain Pandas Agent 

Using LangChain Pandas Agent, we can perform a variety of time series analysis techniques, including: 

  • Trend Analysis: By applying techniques like moving averages and exponential smoothing, we can identify and analyze trends in time series data. 
  • Seasonality Analysis: The agent provides tools to detect and analyze seasonal patterns within time series data, helping us understand recurring trends. 
  • Forecasting: With the help of advanced forecasting models like ARIMA and SARIMA, Lang Chain Pandas Agent enables us to make predictions based on historical time series data. 

LLMs in Action with LangChain Agents

Suppose you are using LangChain, a popular data analysis platform. LangChain’s Pandas Agent seamlessly integrates LLMs into your existing workflows. Here is how: 

  1. Load your time series data: Simply upload your data into LangChain as you normally would. 
  2. Engage the LLM: Activate LangChain’s Pandas Agent, your LLM-powered co-pilot. 
  3. Ask away: Fire away your questions in plain English. “What factors are most likely to influence next quarter’s sales?” or “Is there a seasonal pattern in customer churn?” The LLM will analyze your data and deliver clear, concise answers. 


Learn to build custom chatbots using LangChain


Now Let’s explore Tesla’s stock performance over the past year and demonstrate how Language Models (LLMs) can be utilized for data analysis and unveil valuable insights into market trends.

To begin, we download the dataset and import it into our code editor using the following snippet:



Dataset Preview

Below are the first five rows of our dataset


LangChain Agents_Data Preview


Next, let’s install and import important libraries from LangChain that are instrumental in data analysis.



Following that, we will create a LangChain Pandas DataFrame agent utilizing OpenAI’s API.


With just these few lines of code executed, your LLM-based agent is now primed to extract valuable insights using simple language commands.

Initial Understanding of Data



Lagchain agents - Initial Understanding of Data - Prompt



The analysis of Tesla’s closing stock prices reveals that the average closing price was $217.16. There was a standard deviation of $37.73, indicating some variation in the daily closing prices. The minimum closing price was $142.05, while the maximum reached $293.34.

This comprehensive overview offers insights into the distribution and fluctuation of Tesla’s stock prices during the period analyzed.



Langchain agents - Initial Understanding of Data - Prompt 2



The daily change in Tesla’s closing stock price is calculated, providing valuable insights into its day-to-day fluctuations. The average daily change, computed at 0.0618, signifies the typical amount by which Tesla’s closing stock price varied over the specified period.

This metric offers investors and analysts a clear understanding of the level of volatility or stability exhibited by Tesla’s stock daily, aiding in informed decision-making and risk assessment strategies.

Detecting Anomalies



Langchain agents - Detecting Anomalies - Prompt



In the realm of anomaly detection within financial data, the absence of outliers in closing prices, as determined by the 1.5*IQR rule, is a notable finding. This suggests that within the dataset under examination, there are no extreme values that significantly deviate from the norm.

However, it is essential to underscore that while this statistical method provides a preliminary assessment, a comprehensive analysis should incorporate additional factors and context to conclusively ascertain the presence or absence of outliers.

This comprehensive approach ensures a more nuanced understanding of the data’s integrity and potential anomalies, thus aiding in informed decision-making processes within the financial domain.

Visualizing Data



Langchain agents - Visualizing Data - Prompt


Langchain agents - Visualizing Data - Graph



The chart above depicts the daily closing price of Tesla’s stock plotted over the past year. The horizontal x-axis represents the dates, while the vertical y-axis shows the corresponding closing prices in USD. Each data point is connected by a line, allowing us to visualize trends and fluctuations in the stock price over time. 

By analyzing this chart, we can identify trends like upward or downward movements in Tesla’s stock price. Additionally, sudden spikes or dips might warrant further investigation into potential news or events impacting the stock market.




Langchain agents - Forecasting - Prompt



Even with historical data, predicting the future is a complex task for Large Language Models. Large language models excel at analyzing information and generating text, they cannot reliably forecast stock prices. The stock market is influenced by many unpredictable factors, making precise predictions beyond historical trends difficult.

The analysis reveals an average price of $217.16 with some variation, but for a more confident prediction of Tesla’s price next month, human experts and consideration of current events are crucial.

Key Findings



Langchain agents - Key Findings - Prompt



The generated natural language summary encapsulates the essential insights gleaned from the data analysis. It underscores the stock’s average price, revealing its range from $142.05 to $293.34. Notably, the analysis highlights the stock’s low volatility, a significant metric for investors gauging risk.

With a standard deviation of $37.73, it paints a picture of stability amidst market fluctuations. Furthermore, the observation that most price changes are minor, averaging just 0.26%, provides valuable context on the stock’s day-to-day movements.

This concise summary distills complex data into digestible nuggets, empowering readers to grasp key findings swiftly and make informed decisions.

Limitations and Considerations 

While LLMs offer significant advantages in time series analysis, it is essential to be aware of its limitations. These include the lack of domain-specific knowledge, sensitivity to input wording, biases in training data, and a limited understanding of context.

Data scientists must validate responses with domain expertise, frame questions carefully, and remain vigilant about biases and errors. 

  • LLMs are most effective as a supplementary tool. They can be an asset for uncovering hidden patterns and providing context, but they should not be the sole basis for decisions, especially in critical areas like finance. 
  • Combining LLMs with traditional time series models can be a powerful approach. This leverages the strengths of both methods – the ability of LLMs to handle complex relationships and the interpretability of traditional models. 

Overall, LLMs offer exciting possibilities for time series analysis, but it is important to be aware of their limitations and use them strategically alongside other tools for the best results.

Best Practices for Using LLMs in Time Series Analysis 

To effectively utilize LLMs like ChatGPT or Langchain in time series analysis, the following best practices are recommended: 

  • Combine LLM’s insights with domain expertise to ensure accuracy and relevance. 
  • Perform consistency checks by asking LMMs multiple variations of the same question. 
  • Verify critical information and predictions with reliable external sources. 
  • Use LLMs iteratively to generate ideas and hypotheses that can be refined with traditional methods. 
  • Implement bias mitigation techniques to reduce the risk of biased responses. 
  • Design clear prompts specifying the task and desired output. 
  • Use a zero-shot approach for simpler tasks, and fine-tune for complex problems. 


Explore a hands-on curriculum that helps you build custom LLM applications!


LLMs: A Powerful Tool for Data Analytics

In summary, Large Language Models (LLMs) represent a significant shift in data analysis, offering an accessible avenue to obtain desired insights and narratives. The examples displayed highlight the power of adept prompting in unlocking valuable interpretations.

However, this is merely the tip of the iceberg. With a deeper grasp of effective prompting strategies, users can unleash a wealth of analyses, comparisons, and visualizations.

Mastering the art of effective prompting allows individuals to navigate their data with the skill of seasoned analysts, all thanks to the transformative influence of LLMs.


May 23, 2024

Word embeddings provide a way to present complex data in a way that is understandable by machines. Hence, acting as a translator, it converts human language into a machine-readable form. Their impact on ML tasks has made them a cornerstone of AI advancements.

These embeddings, when particularly used for natural language processing (NLP) tasks, are also referred to as LLM embeddings. In this blog, we will focus on these embeddings in LLM and explore how they have evolved over time within the world of NLP, each transformation being a result of technological advancement and progress.

This journey of continuous evolution of LLM embeddings is key to the enhancement of large language models performance and its improved understanding of the human language. Before we take a trip through the journey of embeddings from the beginning, let’s revisit the impact of embeddings on LLMs.


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Impact of embeddings on LLMs

It is the introduction of embeddings that has transformed LLMs over time from basic text processors to powerful tools that understand language. They have empowered language models to move beyond tasks of simple text manipulation to generate complex and contextually relevant content.

With a deeper understanding of the human language, LLM embeddings have also facilitated these models to generate outputs with greater accuracy. Hence, in their own journey of evolution through the years, embeddings have transformed LLMs to become more efficient and creative, generating increasingly innovative and coherent responses.


Read on to understand the role of embeddings in generative AI


Let’s take a step back and travel through the journey of LLM embeddings from the start to the present day, understanding their evolution every step of the way.

Growth Stages of Word Embeddings

Embeddings have revolutionized the functionality and efficiency of LLMs. The journey of their evolution has empowered large language models to do much more with the content. Let’s get a glimpse of the journey of LLM embeddings to understand the story behind the enhancement of LLMs.


Evolution of LLM embeddings from word embeddings
Stages in the evolution of LLM embeddings


Stage 1: Traditional vector representations

The earliest word representations were in the form of traditional vectors for machines, where words were treated as isolated entities within a text. While it enabled machines to read and understand words, it failed to capture the contextual relationships between words.

Techniques present in this era of language models included:

One-hot encoding

It converts categorical data into a machine-readable format by creating a new binary feature for each category of a data point. It allows ML models to work with data but in a limited manner. Moreover, the technique is more suited to numerical data than textual input.

Bag-of-words (BoW)

This technique focuses on summarizing textual data by creating a simple feature for each word in the input data. BoW does not focus on the order of words in a text. Hence, while it is helpful to develop a basic understanding of a document, it is limited in forming a connection between words to grasp a deeper meaning.

Stage 2: Introduction of neural networks

The next step for LLM embeddings was the introduction of neural networks to capture the contextual information within the data.


Here’s a comprehensive guide to understanding neural networks


New techniques to translate data for machines were used using neural networks, which primarily included:

Self-Organizing Maps (SOMs)

These are useful to explore high-dimensional data, like textual information that has many features. SOMs work to bring down the information into a 2-dimensional map where similar data points form clusters, providing a starting point for advanced embeddings.

Simple Recurrent Networks (SRNs)

The strength of SRNs lies in their ability to handle sequences like text. They function by remembering past inputs to learn more contextual information. However, with long sequences, the networks failed to capture the intricate nuances of language.

Stage 3: The rise of word embeddings

It marks one of the major transitions in the history of LLM embeddings. The idea of word embeddings brought forward the vector representation of words. It also resulted in the formation of more refined word clusters in the three-dimensional space, capturing the semantic relationship between words in a better way.

Some popular word embedding models are listed below.


It is a word embedding technique that considers the surrounding words in a text and their co-occurrence to determine the complete contextual information.

Using this information, Word2Vec creates a unique vector representation of each word, creating improved clusters for similar words. This allows machines to grasp the nuances of language and perform tasks like machine translation and text summarization more effectively.

Global Vectors for Word Representation (GloVe)

It takes on a statistical approach in determining the contextual information of words and analyzing how effectively words contribute to the overall meaning of a document.

With a broader analysis of co-occurrences, GloVe captures the semantic similarity and any analogies in the data. It creates informative word vectors that enhance tasks like sentiment analysis and text classification.


This word embedding technique involves handling out-of-vocabulary (OOV) words by incorporating subword information. It functions by breaking down words into smaller units called n-grams. FastText creates representations by analyzing the occurrences of n-grams within words.

Stage 4: The emergence of contextual embeddings

This stage is marked by embeddings and gathering contextual information after the analysis of surrounding words and sentences. It creates a dynamic representation of words based on the specific context in which they appear. The era of contextual embeddings has evolved in the following manner:

Transformer-based models

The use of transformer-based models like BERT has boosted the revolution of embeddings. Using a transformer architecture, a model like BERT generates embeddings that capture both contextual and syntactic information, leading to highly enhanced performance on various NLP tasks.


Navigate transformer models to understand how they will shape the future of NLP


Multimodal embeddings

As data complexity has increased, embeddings are also created to cater to the various forms of information like text, image, audio, and more. Models like OpenAI’s CLIP (Contrastive Language-Image Pretraining) and Vision Transformer (ViT) enable joint representation learning, allowing embeddings to capture cross-modal relationships.

Transfer Learning and Fine-Tuning

Techniques of transfer learning and fine-tuning pre-trained embeddings have also facilitated the growth of embeddings since they eliminate the need for training from scratch. Leveraging these practices results in more specialized LLMs dealing with specific tasks within the realm of NLP.

Hence, the LLM embeddings started off from traditional vector representations and have evolved from simple word embeddings to contextual embeddings over time. While we now understand the different stages of the journey of embeddings in NLP tasks, let’s narrow our lens towards a comparative look at things.


Read more about fine-tuning LLMs


Through a lens of comparative analysis

Embeddings have played a crucial role in NLP tasks to enhance the accuracy of translation from human language to machine-readable form. With context and meaning as major nuances of human language, embeddings have evolved to apply improved techniques to generate the closest meaning of textual data for ML tasks.

A comparative analysis of some important stages of evolution for LLM embeddings presents a clearer understanding of the aspects that have improved and in what ways.

Word embeddings vs contextual embeddings

Word embeddings and contextual embeddings are both techniques used in NLP to represent words or phrases as numerical vectors. They differ in the way they capture information and the context in which they operate.


LLM Embeddings: Word embeddings vs contextual embeddings
Comparison of word and contextual embeddings at a glance – Source: ResearchGate


Word embeddings represent words in a fixed-dimensional vector space, giving each unit a unique code that presents its meaning. These codes are based on co-occurrence patterns or global statistics, where each word’s code has a single vector representation regardless of its context.

In this way, word embeddings capture the semantic relationships between words, allowing for tasks like word similarity and analogy detection. They are particularly useful when the meaning of a word remains relatively constant across different contexts.

Popular word embedding techniques include Word2Vec and GloVe.

On the other hand, contextual embeddings consider the surrounding context of a word or phrase, creating a more contextualized vector representation. It enables them to capture the meaning of words based on the specific context in which they appear, allowing for more nuanced and dynamic representations.

Contextual embeddings are trained using deep neural networks. They are particularly useful for tasks like sentiment analysis, machine translation, and question answering, where capturing the nuances of meaning is crucial. Common examples of contextual embeddings include ELMo and BERT.

How generative AI and LLMs work



Hence, it is evident that while word embeddings provide fixed representations in a vector space, contextual embeddings generate more dynamic results based on the surrounding context. The choice between the two depends on the specific NLP task and the level of context sensitivity required.

Unsupervised vs. supervised learning for embeddings

While vector representation and contextual inference remain important factors in the evolution of LLM embeddings, the lens of comparative analysis also highlights another aspect for discussion. It involves the different approaches to train embeddings. The two main approaches of interest for embeddings include unsupervised and supervised learning.


word embeddings - training approaches
Visually representing unsupervised and supervised learning – Source: ResearchGate


As the name suggests, unsupervised learning is a type of approach that allows the model to learn patterns and analyze massive amounts of text without any labels or guidance. It aims to capture the inherent structure of the data by finding meaningful representations without any specific task in mind.

Word2Vec and GloVe use unsupervised learning, focusing on how often words appear together to capture the general meaning. They use techniques like neural networks to learn word embeddings based on co-occurrence patterns in the data.

Since unsupervised learning does not require labeled data, it is easier to execute and manage. It is suitable for tasks like word similarity, analogy detection, and even discovering new relationships between words. However, it is limited in its accuracy, especially for words with multiple meanings.

On the contrary, supervised learning requires labeled data where each unit has explicit input-output pairs to train the model. These algorithms train embeddings by leveraging labeled data to learn representations that are optimized for a specific task or prediction.


Learn more about embeddings as building blocks for LLMs


BERT and ELMo are techniques that use supervised learning to capture the meaning of words based on their specific context. These algorithms are trained on large datasets and fine-tuned for specialized tasks like sentiment analysis, named entity recognition, and question answering. However, labeling data can be an expensive and laborious task.

When it comes to choosing the appropriate approach to train embeddings, it depends on the availability of labeled data. Moreover, it is also linked to your needs, where general understanding can be achieved through unsupervised learning but contextual accuracy requires supervised learning.

Another way out is to combine the two approaches when training your embeddings. It can be done by using unsupervised methods to create a foundation and then fine-tuning them with supervised learning for your specific task. This refers to the concept of pre-training of word embeddings.


Explore a hands-on curriculum that helps you build custom LLM applications!


The role of pre-training in embedding quality

Pre-training refers to the unsupervised learning of a model through massive amounts of textual data before its fine-tuning. By analyzing this data, the model builds a strong understanding of how words co-occur, how sentences work, and how context influences meaning.

It plays a crucial role in embedding quality as it determines a model’s understanding of language fundamentals, impacting the accuracy of an LLM to capture contextual information. It leads to improved performance in tasks like sentiment analysis and machine translation. Hence, with more comprehensive pre-training, you get better results from embeddings.



What is next in word embeddings?

The future of LLM embeddings is brimming with potential. With transformer-based and multimodal embeddings, there is immense room for further advancements.

The future is also about making LLM embeddings more accessible and applicable to real-world problems, from education to chatbots that can navigate complex human interactions and much more. Hence, it is about pushing the boundaries of language understanding and communication in AI.

May 10, 2024

In recent years, the landscape of artificial intelligence has been transformed by the development of large language models like GPT-3 and BERT, renowned for their impressive capabilities and wide-ranging applications.

However, alongside these giants, a new category of AI tools is making waves—the small language models (SLMs). These models, such as LLaMA 3, Phi 3, Mistral 7B, and Gemma, offer a potent combination of advanced AI capabilities with significantly reduced computational demands.

Why are Small Language Models Needed?

This shift towards smaller, more efficient models is driven by the need for accessibility, cost-effectiveness, and the democratization of AI technology.

Small language models require less hardware, lower energy consumption, and offer faster deployment, making them ideal for startups, academic researchers, and businesses that do not possess the immense resources often associated with big tech companies.

Moreover, their size does not merely signify a reduction in scale but also an increase in adaptability and ease of integration across various platforms and applications.

Benefits of Small Language Models SLMs | Phi 3

How Small Language Models Excel with Fewer Parameters?

Several factors explain why smaller language models can perform effectively with fewer parameters.

Primarily, advanced training techniques play a crucial role. Methods like transfer learning enable these models to build on pre-existing knowledge bases, enhancing their adaptability and efficiency for specialized tasks.

For example, knowledge distillation from large language models to small language models can achieve comparable performance while significantly reducing the need for computational power.

Moreover, smaller models often focus on niche applications. By concentrating their training on targeted datasets, these models are custom-built for specific functions or industries, enhancing their effectiveness in those particular contexts.

For instance, a small language model trained exclusively on medical data could potentially surpass a general-purpose large model in understanding medical jargon and delivering accurate diagnoses.

However, it’s important to note that the success of a small language model depends heavily on its training regimen, fine-tuning, and the specific tasks it is designed to perform. Therefore, while small models may excel in certain areas, they might not always be the optimal choice for every situation.

Best Small Langauge Models in 2024

Leading Small Language Models | Llama 3 | phi-3
Leading Small Language Models (SLMs)

1. Llama 3 by Meta

LLaMA 3 is an open-source language model developed by Meta. It’s part of Meta’s broader strategy to empower more extensive and responsible AI usage by providing the community with tools that are both powerful and adaptable. This model builds upon the success of its predecessors by incorporating advanced training methods and architecture optimizations that enhance its performance across various tasks such as translation, dialogue generation, and complex reasoning.

Performance and Innovation

Meta’s LLaMA 3 has been trained on significantly larger datasets compared to earlier versions, utilizing custom-built GPU clusters that enable it to process vast amounts of data efficiently.

This extensive training has equipped LLaMA 3 with an improved understanding of language nuances and the ability to handle multi-step reasoning tasks more effectively. The model is particularly noted for its enhanced capabilities in generating more aligned and diverse responses, making it a robust tool for developers aiming to create sophisticated AI-driven applications.

Llama 3 pre-trained model performance
Llama 3 pre-trained model performance – Source: Meta

Why LLaMA 3 Matters

The significance of LLaMA 3 lies in its accessibility and versatility. Being open-source, it democratizes access to state-of-the-art AI technology, allowing a broader range of users to experiment and develop applications. This model is crucial for promoting innovation in AI, providing a platform that supports both foundational and advanced AI research. By offering an instruction-tuned version of the model, Meta ensures that developers can fine-tune LLaMA 3 to specific applications, enhancing both performance and relevance to particular domains.


Learn more about Meta’s Llama 3 


2. Phi 3 By Microsoft

Phi-3 is a pioneering series of SLMs developed by Microsoft, emphasizing high capability and cost-efficiency. As part of Microsoft’s ongoing commitment to accessible AI, Phi-3 models are designed to provide powerful AI solutions that are not only advanced but also more affordable and efficient for a wide range of applications.

These models are part of an open AI initiative, meaning they are accessible to the public and can be integrated and deployed in various environments, from cloud-based platforms like Microsoft Azure AI Studio to local setups on personal computing devices.

Performance and Significance

The Phi 3 models stand out for their exceptional performance, surpassing both similar and larger-sized models in tasks involving language processing, coding, and mathematical reasoning.

Notably, the Phi-3-mini, a 3.8 billion parameter model within this family, is available in versions that handle up to 128,000 tokens of context—setting a new standard for flexibility in processing extensive text data with minimal quality compromise.

Microsoft has optimized Phi 3 for diverse computing environments, supporting deployment across GPUs, CPUs, and mobile platforms, which is a testament to its versatility.

Additionally, these models integrate seamlessly with other Microsoft technologies, such as ONNX Runtime for performance optimization and Windows DirectML for broad compatibility across Windows devices.

Phi 3 family comparison gemma 7b mistral 7b mixtral llama 3
Phi-3 family comparison with Gemma 7b, Mistral 7b, Mixtral 8x7b, Llama 3 – Source: Microsoft

Why Does Phi 3 Matter?

The development of Phi 3 reflects a significant advancement in AI safety and ethical AI deployment. Microsoft has aligned the development of these models with its Responsible AI Standard, ensuring that they adhere to principles of fairness, transparency, and security, making them not just powerful but also trustworthy tools for developers.

3. Mixtral 8x7B by Mistral AI

Mixtral, developed by Mistral AI, is a groundbreaking model known as a Sparse Mixture of Experts (SMoE). It represents a significant shift in AI model architecture by focusing on both performance efficiency and open accessibility.

Mistral AI, known for its foundation in open technology, has designed Mixtral to be a decoder-only model, where a router network selectively engages different groups of parameters, or “experts,” to process data.

This approach not only makes Mixtral highly efficient but also adaptable to a variety of tasks without requiring the computational power typically associated with large models.


Explore the showdown of 7B LLMs – Mistral 7B vs Llama-2 7B

Performance and Innovations

Mixtral excels in processing large contexts up to 32k tokens and supports multiple languages including English, French, Italian, German, and Spanish.

It has demonstrated strong capabilities in code generation and can be fine-tuned to follow instructions precisely, achieving high scores on benchmarks like the MT-Bench.

What sets Mixtral apart is its efficiency—despite having a total parameter count of 46.7 billion, it effectively utilizes only about 12.9 billion per token, aligning it with much smaller models in terms of computational cost and speed.

Why Does Mixtral Matter?

The significance of Mixtral lies in its open-source nature and its licensing under Apache 2.0, which encourages widespread use and adaptation by the developer community.

This model is not only a technological innovation but also a strategic move to foster more collaborative and transparent AI development. By making high-performance AI more accessible and less resource-intensive, Mixtral is paving the way for broader, more equitable use of advanced AI technologies.

Mixtral’s architecture represents a step towards more sustainable AI practices by reducing the energy and computational costs typically associated with large models. This makes it not only a powerful tool for developers but also a more environmentally conscious choice in the AI landscape.

Large Language Models Bootcamp | LLM

4. Gemma by Google

Gemma is a new generation of open models introduced by Google, designed with the core philosophy of responsible AI development. Developed by Google DeepMind along with other teams at Google, Gemma leverages the foundational research and technology that also gave rise to the Gemini models.

Technical Details and Availability

Gemma models are structured to be lightweight and state-of-the-art, ensuring they are accessible and functional across various computing environments—from mobile devices to cloud-based systems.

Google has released two main versions of Gemma: a 2 billion parameter model and a 7 billion parameter model. Each of these comes in both pre-trained and instruction-tuned variants to cater to different developer needs and application scenarios.

Gemma models are freely available and supported by tools that encourage innovation, collaboration, and responsible usage.

Why Does Gemma Matter?

Gemma models are significant not just for their technical robustness but for their role in democratizing AI technology. By providing state-of-the-art capabilities in an open model format, Google facilitates a broader adoption and innovation in AI, allowing developers and researchers worldwide to build advanced applications without the high costs typically associated with large models.

Moreover, Gemma models are designed to be adaptable, allowing users to tune them for specialized tasks, which can lead to more efficient and targeted AI solutions

Explore a hands-on curriculum that helps you build custom LLM applications!

5. OpenELM Family by Apple

OpenELM is a family of small language models developed by Apple. OpenELM models are particularly appealing for applications where resource efficiency is critical. OpenELM is open-source, offering transparency and the opportunity for the wider research community to modify and adapt the models as needed.

Performance and Capabilities

Despite their smaller size and open-source nature, it’s important to note that OpenELM models do not necessarily match the top-tier performance of some larger, more closed-source models. They achieve moderate accuracy levels across various benchmarks but may lag behind in more complex or nuanced tasks. For example, while OpenELM shows improved performance compared to similar models like OLMo in terms of accuracy, the improvement is moderate.

Why Does OpenELM Matter?

OpenELM represents a strategic move by Apple to integrate state-of-the-art generative AI directly into its hardware ecosystem, including laptops and smartphones.

By embedding these efficient models into devices, Apple can potentially offer enhanced on-device AI capabilities without the need to constantly connect to the cloud.

Apple's Open-Source SLMs family | Phi 3
Apple’s Open-Source SLM family

This not only improves functionality in areas with poor connectivity but also aligns with increasing consumer demands for privacy and data security, as processing data locally minimizes the risk of exposure over networks.

Furthermore, embedding OpenELM into Apple’s products could give the company a significant competitive advantage by making their devices smarter and more capable of handling complex AI tasks independently of the cloud.

How generative AI and LLMs work

This can transform user experiences, offering more responsive and personalized AI interactions directly on their devices. The move could set a new standard for privacy in AI, appealing to privacy-conscious consumers and potentially reshaping consumer expectations in the tech industry.

The Future of Small Language Models

As we dive deeper into the capabilities and strategic implementations of small language models, it’s clear that the evolution of AI is leaning heavily towards efficiency and integration. Companies like Apple, Microsoft, and Google are pioneering this shift by embedding advanced AI directly into everyday devices, enhancing user experience while upholding stringent privacy standards.

This approach not only meets the growing consumer demand for powerful, yet private technology solutions but also sets a new paradigm in the competitive landscape of tech companies.

May 7, 2024