Learn to build large language model applications: vector databases, langchain, fine tuning and prompt engineering. Learn more

Language is the basis for human interaction and communication. Speaking and listening are the direct by-products of human reliance on language. While humans can use language to understand each other, in today’s digital world, they must also interact with machines.

The answer lies in large language models (LLMs) – machine-learning models that empower machines to learn, understand, and interact using human language. Hence, they open a gateway to enhanced and high-quality human-computer interaction.

Let’s understand large language models further.

What are large language models?

Imagine a computer program that’s a whiz with words, capable of understanding and using language in fascinating ways. That’s essentially what an LLM is! Large language models are powerful AI-powered language tools trained on massive amounts of text data, like books, articles, and even code.

By analyzing this data, LLMs become experts at recognizing patterns and relationships between words. This allows them to perform a variety of impressive tasks, like:

Creative text generation

LLMs can generate different creative text formats, crafting poems, scripts, musical pieces, emails, and even letters in various styles. From a catchy social media post to a unique story idea, these language models can pull you out of any writer’s block. Some LLMs, like LaMDA by Google AI, can help you brainstorm ideas and even write different creative text formats based on your initial input.

Speak many languages

Since language is the area of expertise for LLMs, the models are trained to work with multiple languages. It enables them to understand and translate languages with impressive accuracy. For instance, Microsoft’s Translator powered by LLMs can help you communicate and access information from all corners of the globe.


Large language model bootcamp


Information powerhouse

With extensive training dataset and diversity of information, LLMs become information powerhouses with quick answers to all your queries. They are highly advanced search engines that can provide accurate and contextually relevant information to your prompts.

Like Megatron-Turing NLG from NVIDIA can analyze vast amounts of information and summarize it in a clear and concise manner. This can help you gain insights and complete tasks more efficiently.

As you kickstart your journey of understanding LLMs, don’t forget to spend some time with this playlist!



LLMs are constantly evolving, with researchers developing new techniques to unlock their full potential. These powerful language tools hold immense promise for various applications, from revolutionizing communication and content creation to transforming the way we access and understand information.

As LLMs continue to learn and grow, they’re poised to be a game-changer in the world of language and artificial intelligence.

While this is a basic concept of LLMs, they are a very vast concept in the world of generative AI and beyond. This blog aims to provide in-depth guidance in your journey to understand large language models. Let’s take a look at all you need to know about LLMs.

A roadmap to building LLM applications

Before we dig deeper into the structural basis and architecture of large language models, let’s look at their practical applications and understand the basic roadmap to building them.


Explore the outline of a roadmap that will guide you in learning about building and deploying LLMs. Read more about it here.


LLM applications are important for every enterprise that aims to thrive in today’s digital world. From reshaping software development to transforming the finance industry, large language models have redefined human-computer interaction in all industrial fields.

However, the application of LLM is not just limited to technical and financial aspects of business. The assistance of large language models has upscaled the legal career of lawyers with ease of documentation and contract management.


Here’s your guide to creating personalized Q&A chatbots


While the industrial impact of LLMs is paramount, the most prominent impact of large language models across all fields has been through chatbots. Every profession and business has reaped the benefits of enhanced customer engagement, operational efficiency, and much more through LLM chatbots.

Here’s a guide to the building techniques and real-life applications of chatbots using large language models: Guide to LLM chatbots

LLMs have improved the traditional chatbot design, offering enhanced conversational ability and better personalization. With the advent of OpenAI’s GPT-4, Google AI’s Gemini, and Meta AI’s LLaMA, LLMs have transformed chatbots to become smarter and a more useful tool for modern-day businesses.

Hence, LLMs have emerged as a useful tool for enterprises, offering advanced data processing and communication for businesses with their machine-learning models. If you are looking for a suitable large language model for your organization, the first step is to explore the available options in the market.

Navigating through large language models in the market

The modern market is swamped with different LLMs for you to choose from. With continuous advancements and model updates, the landscape is constantly evolving to introduce improved choices for businesses. Hence, you must carefully explore the different LLMs in the market before deploying an application for your business.


Learn to build and deploy custom LLM applications for your business


Below is a list of LLMs you can find in the market today.


The list must start with the very famous ChatGPT. Developed by OpenAI, it is a general-purpose LLM that is trained on a large dataset, consisting of text and code. Its instant popularity sparked a widespread interest in LLMs and their potential applications.

While people explored cheat sheets to master ChatGPT usage, it also initiated a debate on the ethical impacts of such a tool in different fields, particularly education. However, despite the concerns, ChatGPT set new records by reaching 100 million monthly active users in just two months.

This tool also offers plugins as supplementary features that enhance the functionality of ChatGPT. We have created a list of the best ChatGPT plugins that are well-suited for data scientists. Explore these to get an idea of the computational capabilities that ChatGPT can offer.

Here’s a guide to the best practices you can follow when using ChatGPT.



Mistral 7b

It is a 7.3 billion parameter model developed by Mistral AI. It incorporates a hybrid approach of transformers and recurrent neural networks (RNNs), offering long-term memory and context awareness for tasks. Mistral 7b is a testament to the power of innovation in the LLM domain.

Here’s an article that explains the architecture and performance of Mistral 7b in detail. You can explore its practical applications to get a better understanding of this large language model.


Designed by Microsoft, Phi-2 has a transformer-based architecture that is trained on 1.4 trillion tokens. It excels in language understanding and reasoning, making it suitable for research and development. With only 2.7 billion parameters, it is a relatively smaller LLM, making it useful for research and development.

You can read more about the different aspects of Phi-2 here.

Llama 2

It is an open-source large language model that varies in scale, ranging from 7 billion to a staggering 70 billion parameters. Meta developed this LLM by training it on a vast dataset, making it suitable for developers, researchers, and anyone interested in their potential.

Llama 2 is adaptable for tasks like question answering, text summarization, machine translation, and code generation. Its capabilities and various model sizes open up potential for diverse applications, focusing on efficient content generation and automating tasks.


Read about the 6 different methods to access Llama 2


Now that you have an understanding of the different LLM applications and their power in the field of content generation and human-computer communication, let’s explore the architectural basis of LLMs.

Emerging frameworks for large language model applications

LLMs have revolutionized the world of natural language processing (NLP), empowering the ability of machines to understand and generate human-quality text. The wide range of applications of these large language models are made accessible through different user-friendly frameworks.


orchestration framework for large language models
An outlook of the LLM orchestration framework


Let’s look at some prominent frameworks for LLM applications.

LangChain for LLM application development

LangChain is a useful framework that simplifies the LLM application development process. It offers pre-built components and a user-friendly interface, enabling developers to focus on the core functionalities of their applications.

LangChain breaks down LLM interactions into manageable building blocks called components and chains. Thus, allowing you to create applications without needing to be an LLM expert. Its major benefits include a simplified development process, flexibility in data integration, and the ability to combine different components for a powerful LLM.

With features like chains, libraries, and templates, the development of large language models is accelerated and code maintainability is promoted. Thus, making it a valuable tool to build innovative LLM applications. Here’s a comprehensive guide exploring the power of LangChain.

You can also explore the dynamics of the working of agents in LangChain.

LlamaIndex for LLM application development

It is a special framework designed to build knowledge-aware LLM applications. It emphasizes on integrating user-provided data with LLMs, leveraging specific knowledge bases to generate more informed responses. Thus, LlamaIndex produces results that are more informed and tailored to a particular domain or task.

With its focus on data indexing, it enhances the LLM’s ability to search and retrieve information from large datasets. With its security and caching features, LlamaIndex is designed to uncover deeper insights in text exploration. It also focuses on ensuring efficiency and data protection for developers working with large language models.

Moreover, its advanced query interfaces make it a unique orchestration framework for LLM application development. Hence, it is a valuable tool for researchers, data analysts, and anyone who wants to unlock the knowledge hidden within vast amounts of textual data using LLMs.

Hence, LangChain and LlamaIndex are two useful orchestration frameworks to assist you in the LLM application development process. Here’s a guide explaining the role of these frameworks in simplifying the LLM apps.

Here’s a webinar introducing you to the architectures for LLM applications, including LangChain and LlamaIndex:



Understand the key differences between LangChain and LlamaIndex


The architecture of large language model applications

While we have explored the realm of LLM applications and frameworks that support their development, it’s time to take our understanding of large language models a step ahead.


architecture for large language models
An outlook of the LLM architecture


Let’s dig deeper into the key aspects and concepts that contribute to the development of an effective LLM application.

Transformers and attention mechanisms

The concept of transformers in neural networks has roots stretching back to the early 1990s with Jürgen Schmidhuber’s “fast weight controller” model. However, researchers have constantly worked towards the advancement of the concept, leading to the rise of transformers as the dominant force in natural language processing

It has paved the way for their continued development and remarkable impact on the field. Transformer models have revolutionized NLP with their ability to grasp long-range connections between words because understanding the relationship between words across the entire sentence is crucial in such applications.


Read along to understand different transformer architectures and their uses


While you understand the role of transformer models in the development of NLP applications, here’s a guide to decoding the transformers further by exploring their underlying functionality using an attention mechanism. It empowers models to produce faster and more efficient results for their users.




While transformer models form the powerful machine architecture to process language, they cannot directly work with words. Transformers rely on embeddings to create a bridge between human language and its numerical representation for the machine model.

Hence, embeddings take on the role of a translator, making words comprehendible for ML models. It empowers machines to handle large amounts of textual data while capturing the semantic relationships in them and understanding their underlying meaning.

Thus, these embeddings lead to the building of databases that transformers use to generate useful outputs in NLP applications. Today, embeddings have also developed to present new ways of data representation with vector embeddings, leading organizations to choose between traditional and vector databases.

While here’s an article that delves deep into the comparison of traditional and vector databases, let’s also explore the concept of vector embeddings.

A glimpse into the realm of vector embeddings

These are a unique type of embedding used in natural language processing which converts words into a series of vectors. It enables words with similar meanings to have similar vector representations, producing a three-dimensional map of data points in the vector space.


Explore the role of vector embeddings in generative AI


Machines traditionally struggle with language because they understand numbers, not words. Vector embeddings bridge this gap by converting words into a numerical format that machines can process. More importantly, the captured relationships between words allow machines to perform NLP tasks like translation and sentiment analysis more effectively.

Here’s a video series providing a comprehensive exploration of embeddings and vector databases.

Vector embeddings are like a secret language for machines, enabling them to grasp the nuances of human language. However, when organizations are building their databases, they must carefully consider different factors to choose the right vector embedding model for their data.

However, database characteristics are not the only aspect to consider. Enterprises must also explore the different types of vector databases and their features. It is also a useful tactic to navigate through the top vector databases in the market.

Thus, embeddings and databases work hand-in-hand in enabling transformers to understand and process human language. These developments within the world of LLMs have also given rise to the idea of prompt engineering. Let’s understand this concept and its many facets.

Prompt engineering

It refers to the art of crafting clear and informative prompts when one interacts with large language models. Well-defined instructions have the power to unlock an LLM’s complete potential, empowering it to generate effective and desired outputs.

Effective prompt engineering is crucial because LLMs, while powerful, can be like complex machines with numerous functionalities. Clear prompts bridge the gap between the user and the LLM. Specifying the task, including relevant context, and structuring the prompt effectively can significantly improve the quality of the LLM’s output.

With the growing dominance of LLMs in today’s digital world, prompt engineering has become a useful skill to hone for individuals. It has led to increased demand for skilled, prompt engineers in the job market, making it a promising career choice for people. While it’s a skill to learn through experimentation, here is a 10-step roadmap to kickstart the journey.

prompt engineering architecture
Explaining the workflow for prompt engineering

Now that we have explored the different aspects contributing to the functionality of large language models, it’s time we navigate the processes for optimizing LLM performance.

Optimizing LLM performance

As businesses work with the design and use of different LLM applications, it is crucial to ensure the use of their full potential. It requires them to optimize LLM performance, creating enhanced accuracy, efficiency, and relevance of LLM results. Some common terms associated with the idea of optimizing LLMs are listed below:

Dynamic few-shot prompting

Beyond the standard few-shot approach, it is an upgrade that selects the most relevant examples based on the user’s specific query. The LLM becomes a resourceful tool, providing contextually relevant responses. Hence, dynamic few-shot prompting enhances an LLM’s performance, creating more captivating digital content.


How generative AI and LLMs work


Selective prediction

It allows LLMs to generate selective outputs based on their certainty about the answer’s accuracy. It enables the applications to avoid results that are misleading or contain incorrect information. Hence, by focusing on high-confidence outputs, selective prediction enhances the reliability of LLMs and fosters trust in their capabilities.

Predictive analytics

In the AI-powered technological world of today, predictive analytics have become a powerful tool for high-performing applications. The same holds for its role and support in large language models. The analytics can identify patterns and relationships that can be incorporated into improved fine-tuning of LLMs, generating more relevant outputs.

Here’s a crash course to deepen your understanding of predictive analytics!



Chain-of-thought prompting

It refers to a specific type of few-shot prompting that breaks down a problem into sequential steps for the model to follow. It enables LLMs to handle increasingly complex tasks with improved accuracy. Thus, chain-of-thought prompting improves the quality of responses and provides a better understanding of how the model arrived at a particular answer.


Read more about the role of chain-of-thought and zero-shot prompting in LLMs here


Zero-shot prompting

Zero-shot prompting unlocks new skills for LLMs without extensive training. By providing clear instructions through prompts, even complex tasks become achievable, boosting LLM versatility and efficiency. This approach not only reduces training costs but also pushes the boundaries of LLM capabilities, allowing us to explore their potential for new applications.

While these terms pop up when we talk about optimizing LLM performance, let’s dig deeper into the process and talk about some key concepts and practices that support enhanced LLM results.

Fine-tuning LLMs

It is a powerful technique that improves LLM performance on specific tasks. It involves training a pre-trained LLM using a focused dataset for a relevant task, providing the application with domain-specific knowledge. It ensures that the model output is refined for that particular context, making your LLM application an expert in that area.

Here is a detailed guide that explores the role, methods, and impact of fine-tuning LLMs. While this provides insights into ways of fine-tuning an LLM application, another approach includes tuning specific LLM parameters. It is a more targeted approach, including various parameters like the model size, temperature, context window, and much more.

Moreover, among the many techniques of fine-tuning, Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF) are popular methods of performance enhancement. Here’s a quick glance at comparing the two ways for you to explore.


RLHF v DPO - optimizing large language models
A comparative analysis of RLHF and DPO – Read more and in detail here


Retrieval augmented generation (RAG)

RAG or retrieval augmented generation is a LLM optimization technique that particularly addresses the issue of hallucinations in LLMs. An LLM application can generate hallucinated responses when prompted with information not present in their training set, despite being trained on extensive data.


Master your knowledge of retrieval augmented generation


The solution with RAG creates a bridge over this information gap, offering a more flexible approach to adapting to evolving information. Here’s a guide to assist you in implementing RAG to elevate your LLM experience.


Advanced RAG to elevate large language models
A glance into the advanced RAG to elevate your LLM experience


Hence, with these two crucial approaches to enhance LLM performance, the question comes down to selecting the most appropriate one.

RAG and fine-tuning

Let me share two valuable resources that can help you answer the dilemma of choosing the right technique for LLM performance optimization.

RAG and fine-tuning

The blog provides a detailed and in-depth exploration of the two techniques, explaining the workings of a RAG pipeline and the fine-tuning process. It also focuses on explaining the role of these two methods in advancing the capabilities of LLMs.

RAG vs fine-tuning

Once you are hooked by the importance and impact of both methods, delve into the findings of this article that navigates through the RAG vs fine-tuning dilemma. With a detailed comparison of the techniques, the blog takes it a step ahead and presents a hybrid approach for your consideration as well.


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


While building and optimizing are crucial steps in the journey of developing LLM applications, evaluating large language models is an equally important aspect.

Evaluating LLMs


large language models - Evaluation process to enhance LLM performance
Evaluation process to enhance LLM performance


It is the systematic process of assessing an LLM’s performance, reliability, and effectiveness across various tasks. Usually through a series of tests to gauge its strengths, weaknesses, and suitability for different applications, we can evaluate LLM performance.

It ensures that an LLM application shows the desired functionality while highlighting its areas of strengths and weaknesses. It is an effective way to determine which LLMs are best suited for specific tasks.

Learn more about the simple and easy techniques for evaluating LLMs.



Among the transforming trends of evaluating LLMs, some common aspects to consider during the evaluation process include:

  • Performance Metrics – It includes accuracy, fluency, and coherence to assess the quality of the LLM’s outputs
  • Generalization – It explores how well the LLM performs on unseen data, not just the data it was trained on
  • Robustness – It involves testing the LLM’s resilience against adversarial attacks or output manipulation
  • Ethical Considerations – It considers potential biases or fairness issues within the LLM’s outputs

Explore the top LLM evaluation methods you can use when testing your LLM applications. A key part of the process also involves understanding the challenges and risks associated with large language models.

Challenges and risks of LLMs

Like any other technological tool or development, LLMs also carry certain challenges and risks in their design and implementation. Some common issues associated with LLMs include hallucinations in responses, high toxic probabilities, bias and fairness, data security threats, and lack of accountability.

However, the problems associated with LLMs do not go unaddressed. The answer lies in the best practices you can take on when dealing with LLMs to mitigate the risks, and also in implementing the large language model operations (also known as LLMOps) process that puts special focus on addressing the associated challenges.

Hence, it is safe to say that as you start your LLM journey, you must navigate through various aspects and stages of development and operation to get a customized and efficient LLM application. The key to it all is to take the first step towards your goal – the rest falls into place gradually.

Some resources to explore

To sum it up – here’s a list of some useful resources to help you kickstart your LLM journey!

  • A list of best large language models in 2024
  • An overview of the 20 key technical terms to make you well-versed in the LLM jargon
  • A blog introducing you to the top 9 YouTube channels to learn about LLMs
  • A list of the top 10 YouTube videos to help you kickstart your exploration of LLMs
  • An article exploring the top 5 generative AI and LLM bootcamps

Bonus addition!

If you are unsure about bootcamps – here are some insights into their importance. The hands-on approach and real-time learning might be just the push you need to take your LLM journey to the next level! And it’s not too time-consuming, you’d know the most about LLMs in as much as 40 hours!


As we conclude our LLM exploration journey, take the next step and learn to build customized LLM applications with fellow enthusiasts in the field. Check out our in-person large language models BootCamp and explore the pathway to deepen your understanding of LLMs!
April 18, 2024

The field of artificial intelligence is booming with constant breakthroughs leading to ever-more sophisticated applications. This rapid growth translates directly to job creation. Thus, AI jobs are a promising career choice in today’s world.

As AI integrates into everything from healthcare to finance, new professions are emerging, demanding specialists to develop, manage, and maintain these intelligent systems. The future of AI is bright, and brimming with exciting job opportunities for those ready to embrace this transformative technology.

In this blog, we will explore the top 10 AI jobs and careers that are also the highest-paying opportunities for individuals in 2024.

Top 10 highest-paying AI jobs in 2024

Our list will serve as your one-stop guide to the 10 best AI jobs you can seek in 2024.


10 Highest-Paying AI Jobs in 2024
10 Highest-Paying AI Jobs in 2024


Let’s explore the leading roles with hefty paychecks within the exciting world of AI.

Machine learning (ML) engineer

Potential pay range – US$82,000 to 160,000/yr

Machine learning engineers are the bridge between data science and engineering. They are responsible for building intelligent machines that transform our world. Integrating the knowledge of data science with engineering skills, they can design, build, and deploy machine learning (ML) models.

Hence, their skillset is crucial to transform raw into algorithms that can make predictions, recognize patterns, and automate complex tasks. With growing reliance on AI-powered solutions and digital transformation with generative AI, it is a highly valued skill with its demand only expected to grow. They consistently rank among the highest-paid AI professionals.

AI product manager

Potential pay range – US$125,000 to 181,000/yr

They are the channel of communication between technical personnel and the upfront business stakeholders. They play a critical role in translating cutting-edge AI technology into real-world solutions. Similarly, they also transform a user’s needs into product roadmaps, ensuring AI features are effective, and aligned with the company’s goals.

The versatility of this role demands a background of technical knowledge with a flare for business understanding. The modern-day businesses thriving in the digital world marked by constantly evolving AI technology rely heavily on AI product managers, making it a lucrative role to ensure business growth and success.


Large language model bootcamp


Natural language processing (NLP) engineer

Potential pay range – US$164,000 to 267,000/yr

As the name suggests, these professionals specialize in building systems for processing human language, like large language models (LLMs). With tasks like translation, sentiment analysis, and content generation, NLP engineers enable ML models to understand and process human language.

With the rise of voice-activated technology and the increasing need for natural language interactions, it is a highly sought-after skillset in 2024. Chatbots and virtual assistants are some of the common applications developed by NLP engineers for modern businesses.

Big data engineer

Potential pay range – US$206,000 to 296,000/yr

They operate at the backend to build and maintain complex systems that store and process the vast amounts of data that fuel AI applications. They design and implement data pipelines, ensuring data security and integrity, and developing tools to analyze massive datasets.

This is an important role for rapidly developing AI models as robust big data infrastructures are crucial for their effective learning and functionality. With the growing amount of data for businesses, the demand for big data engineers is only bound to grow in 2024.

Data scientist

Potential pay range – US$118,000 to 206,000/yr

Their primary goal is to draw valuable insights from data. Hence, they collect, clean, and organize data to prepare it for analysis. Then they proceed to apply statistical methods and machine learning algorithms to uncover hidden patterns and trends. The final step is to use these analytic findings to tell a concise story of their findings to the audience.

Hence, the final goal becomes the extraction of meaning from data. Data scientists are the masterminds behind the algorithms that power everything from recommendation engines to fraud detection. They enable businesses to leverage AI to make informed decisions. With the growing AI trend, it is one of the sought-after AI jobs.

Here’s a guide to help you ace your data science interview as you explore this promising career choice in today’s market.


Computer vision engineer

Potential pay range – US$112,000 to 210,000/yr

These engineers specialize in working with and interpreting visual information. They focus on developing algorithms to analyze images and videos, enabling machines to perform tasks like object recognition, facial detection, and scene understanding. Some common applications of it include driving cars, and medical image analysis.

With AI expanding into new horizons and avenues, the role of computer vision engineers is one new position created out of the changing demands of the field. The demand for this role is only expected to grow, especially with the increasing use and engagement of visual data in our lives. Computer vision engineers play a crucial role in interpreting this huge chunk of visual data.

AI research scientist

Potential pay range – US$69,000 to 206,000/yr

The role revolves around developing new algorithms and refining existing ones to make AI systems more efficient, accurate, and capable. It requires both technical expertise and creativity to navigate through areas of machine learning, NLP, and other AI fields.

Since an AI research scientist lays the groundwork for developing next-generation AI applications, the role is not only important for the present times but will remain central to the growth of AI. It’s a challenging yet rewarding career path for those passionate about pushing the frontiers of AI and shaping the future of technology.


Read more about Moondream 2 – a tiny vision language model


Business development manager (BDM)

Potential pay range – US$36,000 to 149,000/yr

They identify and cultivate new business opportunities for AI technologies by understanding the technical capabilities of AI and the specific needs of potential clients across various industries. They act as strategic storytellers who build narratives that showcase how AI can solve real-world problems, ensuring a positive return on investment.

Among the different AI jobs, they play a crucial role in the growth of AI. Their job description is primarily focused on getting businesses to see the potential of AI and invest in its growth, benefiting themselves and society as a whole. Keeping AI growth in view, it is a lucrative career path at the forefront of technological innovation.


How generative AI and LLMs work

Software engineer

Potential pay range – US$66,000 to 168,000/yr

Software engineers have been around the job market for a long time, designing, developing, testing, and maintaining software applications. However, with AI’s growth spurt in modern-day businesses, their role has just gotten more complex and important in the market.

Their ability to bridge the gap between theory and application is crucial for bringing AI products to life. In 2024, this expertise is well-compensated, with software engineers specializing in AI to create systems that are scalable, reliable, and user-friendly. As the demand for AI solutions continues to grow, so too will the need for skilled software engineers to build and maintain them.

Prompt engineer

Potential pay range – US$32,000 to 95,000/yr

They belong under the banner of AI jobs that took shape with the growth and development of AI. Acting as the bridge between humans and large language models (LLMs), prompt engineers bring a unique blend of creativity and technical understanding to create clear instructions for the AI-powered ML models.

As LLMs are becoming more ingrained in various industries, prompt engineering has become a rapidly evolving AI job and its demand is expected to rise significantly in 2024. It’s a fascinating career path at the forefront of human-AI collaboration.



The potential and future of AI jobs

The world of AI is brimming with exciting career opportunities. From the strategic vision of AI product managers to the groundbreaking research of AI scientists, each role plays a vital part in shaping the future of this transformative technology. Some key factors that are expected to mark the future of AI jobs include:

  • a rapid increase in demand
  • growing need for specialization for deeper expertise to tackle new challenges
  • human-AI collaboration to unleash the full potential
  • increasing focus on upskilling and reskilling to stay relevant and competitive


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


If you’re looking for a high-paying and intellectually stimulating career path, the AI field offers a wealth of options. This blog has just scratched the surface – consider this your launchpad for further exploration. With the right skills and dedication, you can be a part of the revolution and help unlock the immense potential of AI.

April 16, 2024

While language models in generative AI focus on textual data, vision language models (VLMs) bridge the gap between textual and visual data. Before we explore Moondream 2, let’s understand VLMs better.

Understanding vision language models

VLMs combine computer vision (CV) and natural language processing (NLP), enabling them to understand and connect visual information with textual data.

Some key capabilities of VLMs include image captioning, visual question answering, and image retrieval. It learns these tasks by training on datasets that pair images with their corresponding textual description. There are several large vision language models available in the market including GPT-4v, LLaVA, and BLIP-2.


Large language model bootcamp


However, these are large vision models requiring heavy computational resources to produce effective results, and that too at slow inference speeds. The solution has been presented in the form of small VLMs that provide a balance between efficiency and performance.

In this blog, we will look deeper into Moondream 2, a small vision language model.

What is Moondream 2?

Moondream 2 is an open-source vision language model. With only 1.86 billion parameters, it is a tiny VLM with weights from SigLIP and Phi-1.5. It is designed to operate seamlessly on devices with limited computational resources.


Weights for Moondream 2
Weights for Moondream 2


Let’s take a closer look at the defined weights for Moondream2.

SigLIP (Sigmoid Loss for Language Image Pre-Training)

It is a newer and simpler method that helps the computer learn just by looking at pictures and their captions, one at a time, making it faster and more effective, especially when training with lots of data. It is similar to a CLIP (Contrastive Language–Image Pre-training) model.

However, Moondream 2 has replaced softmax loss in CLIP with a simple pairwise sigmoid loss. The change ensures better performance because sigmoid loss only focuses on image-text pairs. Without the need for a global view of all pairwise data within a batch, the process becomes faster and more efficient.


Learn computer vision using Python



It is a small language model with 1.3 billion parameters and transformer-based architecture. Developed by Microsoft, the model was trained on 30 billion words, including information from its predecessor, Phi-1 and 20 billion words artificially created by another AI model, GPT-3.5.

With its unique training method, Phi-1.5 has mastered different skills required of a language model, like understanding common sense, following complex instructions, comprehending language, and retaining information. Moreover, it even outperforms its larger counterparts.

A closer look into Moondream 2

It is the latest and updated version of the model previously known as Moondream 1. Below is a quick comparison of the two models, highlighting the basic parameters of improvement.


Comparing Moondream 1 and 2
Comparing Moondream 1 and 2


Like typical vision language models, Moondream 2 interprets the visual data to provide textual responses based on the information. Some demos included in its repository highlight its capabilities. Let’s look at these demos below:

Demo 1

Moondream 2 was provided with the following image:


Moondream demo 1


Based on this image, the VLM was asked some relevant questions.

Question: What is the girl doing?

Answer: The girl is sitting at a table and eating a large hamburger.

Question: What color is the girl’s hair?

Answer: The girl’s hair is white.

The interaction with Moondream 2 highlights that the VLM is capable of understanding the basic image properties like the girl’s hair color. Moreover, it can also interpret the actions of the actors in an image from their position in a specific instance.

Demo 2

The VLM was provided with another image.


Moondream demo 2


The demo focused on asking the following:

Question: What is this?

Answer: The image features a computer server rack, which is a large metal structure designed to hold and organize multiple computer components, such as motherboards, cooling systems, and other peripherals. The rack is filled with various computer parts, including multiple computer chips, wires, and other electronic components. The rack is placed on a carpeted floor, and there is a couch in the background, suggesting that the setup is likely in a living or working space.

Question: What is behind the stand?

Answer: There is a brick wall behind the stand.

The above demo highlights the ability of Moondream 2 to explore and interpret complex visual outputs in great detail. The VLM provides in-depth textual information from the visual data. It also presents spacial understanding of the image components.

Hence, Moondream 2 is a promising addition to the world of vision language models with its refined capabilities to interpret visual data and provide in-depth textual output. Since we understand the strengths of the VLM, it is time to explore its drawbacks or weaknesses.


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Limitations of Moondream 2

Before you explore the world of Moondream 2, you must understand its limitations when dealing with visual and textual data.

Generating inaccurate statements

It is important to understand that Moondream 2 may generate inaccurate statements, especially for complex topics or situations requiring real-world understanding. The model might also struggle to grasp subtle details or hidden meanings within instructions.

Presenting unconscious bias

Like any other VLM, Moondream 2 is also a product of the data is it trained on. Thus, it can reflect the biases of the world, perpetuating stereotypes or discriminatory views.

As a user, it’s crucial to be aware of this potential bias and to approach the model’s outputs with a critical eye. Don’t blindly accept everything it generates; use your own judgment and fact-check when necessary.

Mirroring prompts

VLMs will reflect the prompts provided to them. Hence, if a user prompts the model to generate offensive or inappropriate content, the model may comply. It’s important to be mindful of the prompts and avoid asking the model to create anything harmful or hurtful.


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In conclusion…

To sum it up, Moondream 2 is a promising step in the development of vision language models. Powered by its key components and compact size, the model is efficient and fast. However, like any language model we use nowadays, Moondream 2 also requires its users to be responsible for ensuring the creation of useful content.

If you are ready to experiment with Moondream 2 now, install the necessary files and start right away! Here’s a look at what the VLM’s user interface looks like.

April 9, 2024

The modern era of generative AI is now talking about machine unlearning. It is time to understand that unlearning information is as important for machines as for humans to progress in this rapidly advancing world. This blog explores the impact of machine unlearning in improving the results of generative AI.

However, before we dig deeper into the details, let’s understand what is machine unlearning and its benefits.

What is machine unlearning?

As the name indicates, it is the opposite of machine learning. Hence, it refers to the process of getting a trained model to forget information and specific knowledge it has learned during the training phase.

During machine unlearning, an ML model discards previously learned information and or patterns from its knowledge base. The concept is fairly new and still under research in an attempt to improve the overall ML training process.


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A comment on the relevant research

A research paper published by the University of Texas presents machine learning as a paradigm to improve image-to-image generative models. It addresses the gap with a unifying framework focused on implementing machine unlearning to image-specific generative models.

The proposed approach uses encoders in its architecture to enable the model to only unlearn specific information without the need to manipulate the entire model. The research also claims the framework to be generalizable in its application, where the same infrastructure can also be implemented in an encoder-decoder architecture.


A glance at the proposed encoder-only machine unlearning architecture
A glance at the proposed encoder-only machine unlearning architecture – Source: arXiv


The research also highlights that the proposed framework presents negligible performance degradation and produces effective results from their experiments. This highlights the potential of the concept in refining machine-learning processes and generative AI applications.

Benefits of machine unlearning in generative AI

Machine unlearning is a promising aspect for improving generative AI, empowering it to create enhanced results when creating new things like text, images, or music.

Below are some of the key advantages associated with the introduction of the unlearning concept in generative AI.

Ensuring privacy

With a constantly growing digital database, the security and privacy of sensitive information have become a constant point of concern for individuals and organizations. This issue of data privacy also extends to the process of training ML models where the training data might contain some crucial or private data.

In this dilemma, unlearning is a concept that enables an ML model to forget any sensitive information in its database without the need to remove the complete set of knowledge it trained on. Hence, it ensures that the concerns of data privacy are addressed without impacting the integrity of the ML model.


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Enhanced accuracy

In extension, it also results in updating the training data for machine-learning models to remove any sources of error. It ensures that a more accurate dataset is available for the model, improving the overall accuracy of the results.

For instance, if a generative AI model produced images based on any inaccurate information it had learned during the training phase, unlearning can remove that data from its database. Removing that association will ensure that the model outputs are refined and more accurate.

Keeping up-to-date

Another crucial aspect of modern-day information is that it is constantly evolving. Hence, the knowledge is updated and new information comes to light. While it highlights the constant development of data, it also results in producing outdated information.

However, success is ensured in keeping up-to-date with the latest trends of information available in the market. With the machine unlearning concept, these updates can be incorporated into the training data for applications without rebooting the existing training models.


Benefits of machine unlearning
Benefits of machine unlearning


Improved control

Unlearning also allows better control over the training data. It is particularly useful in artistic applications of generative AI. Artists can use the concept to ensure that the AI application unlearns certain styles or influences.

As a result, it offers greater freedom of exploration of artistic expression to create more personalized outputs, promising increased innovation and creativity in the results of generative AI applications.

Controlling misinformation

Generative AI is a powerful tool to spread misinformation through the creation of realistic deepfakes and synthetic data. Machine unlearning provides a potential countermeasure that can be used to identify and remove data linked to known misinformation tactics from generative AI models.

This would make it significantly harder for them to be used to create deceptive content, providing increased control over spreading misinformation on digital channels. It is particularly useful in mitigating biases and stereotypical information in datasets.

Hence, the concept of unlearning opens new horizons of exploration in generative AI, empowering players in the world of AI and technology to reap its benefits.


Here’s a comprehensive guide to build, deploy, and manage ML models


Who can benefit from machine unlearning?

A broad categorization of entities and individuals who can benefit from machine unlearning include:

Privacy advocates

In today’s digital world, individual concern for privacy concern is constantly on the rise. Hence, people are constantly advocating their right to keep personal or crucial information private. These advocates for privacy and data security can benefit from unlearning as it addresses their concerns about data privacy.

Tech companies

Digital progress and development are marked by several regulations like GDPR and CCPA. These standards are set in place to ensure data security and companies must abide by these laws to avoid legal repercussions. Unlearning assists tech companies in abiding by these laws, enhancing their credibility among users as well.

Financial institutions

Financial enterprises and institutions deal with huge amounts of personal information and sensitive data of their users. Unlearning empowers them to remove specific data points from their database without impacting the accuracy and model performance.

AI researchers

AI researchers are frequently facing the impacts of their applications creating biased or inaccurate results. With unlearning, they can target such sources of data points that introduce bias and misinformation into the model results. Hence, enabling them to create more equitable AI systems.


A significant impact of unlearning can come from the work of policymakers. Since the concept opens up new ways to handle information and training datasets, policymakers can develop new regulations to mitigate bias and address privacy concerns. Hence, leading the way for responsible AI development.

Thus, machine unlearning can produce positive changes in the world of generative AI, aiding different players to ensure the development of more responsible and equitable AI systems.


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Future of machine unlearning

To sum it up, machine unlearning is a new concept in the world of generative AI with promising potential for advancement. Unlearning is a powerful tool for developing AI applications and systems but lacks finesse. Researchers are developing ways to target specific information for removal.

For instance, it can assist the development of an improved text-to-image generator to forget a biased stereotype, leading to fairer and more accurate results. Improved techniques allow the isolation and removal of unwanted data points, giving finer control over what the AI forgets.



Overall, unlearning holds immense potential for shaping the future of generative AI. With more targeted techniques and a deeper understanding of these models, unlearning can ensure responsible use of generative AI, promote artistic freedom, and safeguard against the misuse of this powerful technology.

April 8, 2024

In the rapidly growing digital world, AI advancement is driving the transformation toward improved automation, better personalization, and smarter devices. In this evolving AI landscape, every country is striving to make the next big breakthrough.

In this blog, we will explore the global progress of artificial intelligence, highlighting the leading countries of AI advancement in 2024.

Top 9 countries leading AI development in 2024


leaders in AI advancement
Leaders in AI advancement for 2024


Let’s look at the leading 9 countries that are a hub for AI advancement in 2024, exploring their contribution and efforts to excel in the digital world.

The United States of America

Providing a home to the leading tech giants, including OpenAI, Google, and Meta, the United States has been leading the global AI race. The contribution of these companies in the form of GPT-4, Llama 2, Bard, and other AI-powered tools, has led to transformational changes in the world of generative AI.

The US continues to hold its leading position in AI advancement in 2024 with its high concentration of top-tier AI researchers fueled by the tech giants operating from Silicon Valley. Moreover, government support and initiative fosters collaboration, promising the progress of AI in the future.

The recent development of the Biden administration focused on ethical considerations for AI is another proactive approach by the US to ensure suitable regulation of AI advancement. This focus on responsible AI development can be seen as a positive step for the future.


Explore the key trends of AI in digital marketing in 2024



The next leading player in line is China powered by companies like Tencent, Huawei, and Baidu. The new releases, including Tencent’s Hunyuan’s large language model and Huawei’s Pangu, are guiding the country’s AI advancements.

Strategic focus on specific research areas in AI, government funding, and a large population providing a massive database are some of the favorable features that promote the technological development of China in 2024.

Moreover, China is known for its rapid commercialization, bringing AI products rapidly to the market. A subsequent benefit of it is the quick collection of real-world data and user feedback, ensuring further refinement of AI technologies. Thus, making China favorable to make significant strides in the field of AI in 2024.


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The United Kingdom

The UK remains a significant contributor to the global AI race, boasting different avenues for AI advancement, including DeepMind – an AI development lab. Moreover, it hosts world-class universities like Oxford, Cambridge, and Imperial College London which are at the forefront of AI research.

The government also promotes AI advancement through investment and incentives, fostering a startup culture in the UK. It has also led to the development of AI companies like Darktrace and BenevolentAI supported by an ecosystem that provides access to funding, talent, and research infrastructure.

Thus, the government’s commitment and focus on responsible AI along with its strong research tradition, promises a growing future for AI advancement.


With top AI-powered companies like Cohere, Scale AI, and Coveo operating from the country, Canada has emerged as a leading player in the world of AI advancement. The government’s focus on initiatives like the Pan-Canadian Artificial Intelligence Strategy has also boosted AI development in the country.

Moreover, the development of research hubs and top AI talent in institutes like the Montreal Institute for Learning Algorithms (MILA) and the Alberta Machine Intelligence Institute (AMII) promotes an environment of development and innovation. It has also led to collaborations between academia and industry to accelerate AI advancement.

Canada is being strategic about its AI development, focusing on sectors where it has existing strengths, including healthcare, natural resource management, and sustainable development. Thus, Canada’s unique combination of strong research capabilities, ethical focus, and collaborative environment positions it as a prominent player in the global AI race.


While not at the top like the US or China, France is definitely leading the AI research in the European Union region. Its strong academic base has led to the development of research institutes like Inria and the 3IA Institutes, prioritizing long-term advancements in the field of AI.

The French government also actively supports research in AI, promoting the growth of innovative AI startups like Criteo (advertising) and Owkin (healthcare). Hence, the country plays a leading role in focusing on fundamental research alongside practical applications, giving France a significant advantage in the long run.


India is quietly emerging as a significant player in AI research and technology as the Indian government pours resources into initiatives like ‘India AI’, fostering a skilled workforce through education programs. This is fueling a vibrant startup landscape where homegrown companies like SigTuple are developing innovative AI solutions.

What truly sets India apart is its focus on social impact as it focuses on using AI to tackle challenges like healthcare access in rural areas and improve agricultural productivity. India also recognizes the importance of ethical AI development, addressing potential biases to ensure the responsible use of this powerful technology.

Hence, the focus on talent, social good, and responsible innovation makes India a promising contributor to the world of AI advancement in 2024.

Learn more about the top AI skills and jobs in 2024


With an aging population and strict immigration laws, Japanese companies have become champions of automation. It has resulted in the country developing solutions with real-world AI implementation, making it a leading contributor to the field.

While they are heavily invested in AI that can streamline processes and boost efficiency, their approach goes beyond just getting things done. Japan is also focused on collaboration between research institutions, universities, and businesses, prioritizing safety, with regulations and institutes dedicated to ensuring trustworthy AI.

Moreover, the country is a robotics powerhouse, integrating AI to create next-gen robots that work seamlessly alongside humans. So, while Japan might not be the first with every breakthrough, they are surely leading the way in making AI practical, safe, and collaborative.


Germans are at the forefront of a new industrial revolution in 2024 with Industry 4.0. Tech giants like Siemens and Bosch using AI are using AI to supercharge factories with intelligent robots, optimized production lines, and smart logistics systems.

The government also promotes AI advancement through funding for collaborations, especially between academia and industry. The focus on AI development has also led to the initiation of startups like Volocopter, Aleph Alpha, DeepL, and Parloa.

However, the development is also focused on the ethical aspects of AI, addressing potential biases on the technology. Thus, Germany’s focus on practical applications, responsible development, and Industry 4.0 makes it a true leader in this exciting new era.


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The country has made it onto the global map of AI advancement with its strategic approach towards research in the field. The government welcomes international researchers to contribute to their AI development. It has resulted in big names like Google setting up shop there, promoting open collaboration using cutting-edge open-source AI tools.

Some of its notable startups include Biofourmis, Near, Active.Ai, and Osome. Moreover, Singapore leverages AI for applications beyond the tech race. Their ‘Smart Nation’ uses AI for efficient urban planning and improved public services.

In addition to this, with its focus on social challenges and focusing on the ethical use of AI, Singapore has a versatile approach to AI advancement. It makes the country a promising contender to become a leader in AI development in the years to come.



The future of AI advancement

The versatility of AI tools promises a future for the field in all kinds of fields. From personalizing education to aiding scientific discoveries, we can expect AI to play a crucial role in all departments. Moreover, the focus of the leading nations on the ethical impacts of AI ensures an increased aim toward responsible development.

Hence, it is clear that the rise of AI is inevitable. The worldwide focus on AI advancement creates an environment that promotes international collaboration and democratization of AI tools. Thus, leading to greater innovation and better accessibility for all.

April 3, 2024

Knowledge graphs and LLMs are the building blocks of the most recent advancements happening in the world of artificial intelligence (AI). Combining knowledge graphs (KGs) and LLMs produces a system that has access to a vast network of factual information and can understand complex language.

The system has the potential to use this accessibility to answer questions, generate textual outputs, and engage with other NLP tasks. This blog aims to explore the potential of integrating knowledge graphs and LLMs, navigating through the promise of revolutionizing AI.

Introducing knowledge graphs and LLMs

Before we understand the impact and methods of integrating KGs and LLMs, let’s visit the definition of the two concepts.

What are knowledge graphs (KGs)?

They are a visual web of information that focuses on connecting factual data in a meaningful manner. Each set of data is represented as a node with edges building connections between them. This representational storage of data allows a computer to recognize information and relationships between the data points.

KGs organize data to highlight connections and new relationships in a dataset. Moreover, it enabled improved search results as knowledge graphs integrate the contextual information to provide more relevant results.


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What are large language models (LLMs)?

LLMs are a powerful tool within the world of AI using deep learning techniques for general-purpose language generation and other natural language processing (NLP) tasks. They train on massive amounts of textual data to produce human-quality texts.

Large language models have revolutionized human-computer interactions with the potential for further advancements. However, LLMs are limited in the factual grounding of their results. It makes LLMs able to produce high-quality and grammatically accurate results that can be factually inaccurate.


knowledge graphs and LLMs
An overview of knowledge graphs and LLMs – Source: arXiv


Combining KGs and LLMs

Within the world of AI and NLP, integrating the concepts of KGs and LLMs has the potential to open up new avenues of exploration. While knowledge graphs cannot understand language, they are good at storing factual data. Unlike KGs, LLMs excel in language understanding but lack factual grounding.

Combining the two entities brings forward a solution that addresses the weaknesses of both. The strengths of KGs and LLMs cover each concept’s limitations, producing more accurate and better-represented results.

Frameworks to combine KGs and LLMs

It is one thing to talk about combining knowledge graphs and large language models, implementing the idea requires planning and research. So far, researchers have explored three different frameworks aiming to integrate KGs and LLMs for enhanced outputs.

In this section, we will explore these three frameworks that are published as a paper in IEEE Transactions on Knowledge and Data Engineering.


Frameworks for integrating KGs and LLMs
Frameworks for integrating KGs and LLMs – Source: arXiv


KG-enhanced LLMs

This framework focuses on using knowledge graphs for training LLMs. The factual knowledge and relationship links in the KGs become accessible to the LLMs in addition to the traditional textual data during the training phase. A LLM can then learn from the information available in KGs.

As a result, LLMs can get a boost in factual accuracy and grounding by incorporating the data from KGs. It will also enable the models to fact-check the outputs and produce more accurate and informative results.

LLM-augmented KGs

This design shifts the structure of the first framework. Instead of KGs enhancing LLMs, they leverage the reasoning power of large language models to improve knowledge graphs. It makes LLMs smart assistants to improve the output of KGs, curating their information representation.

Moreover, this framework can leverage LLMs to find problems and inconsistencies in information connections of KGs. The high reasoning of LLMs also enables them to infer new relationships in a knowledge graph, enriching its outputs.

This builds a pathway to create more comprehensive and reliable knowledge graphs, benefiting from the reasoning and inference abilities of LLMs.


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Synergized LLMs + KGs

This framework proposes a mutually beneficial relationship between the two AI components. Each entity works to improve the other through a feedback loop. It is designed in the form of a continuous learning cycle between LLMs and KGs.

It can be viewed as a concept that combines the two above-mentioned frameworks into a single design where knowledge graphs enhance language model outputs and LLMs analyze and improve KGs.

It results in a dynamic cycle where KGs and LLMs constantly improve each other. The iterative design of this integration framework leads to a more powerful and intelligent system overall.

While we have looked at the three different frameworks of integration of KGs and LLMs, the synergized LLMs + KGs is the most advanced approach in this field. It promises to unlock the full potential of both entities, supporting the creation of superior AI systems with enhanced reasoning, knowledge representation, and text generation capabilities.


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Future of LLM and KG integration

Combining the powers of knowledge graphs and large language models holds immense potential in various fields. Some plausible possibilities are discussed below.

Educational revolution

With access to knowledge graphs, LLMs can generate personalized educational content for students, encompassing a wide range of subjects and topics. The data can be used to generate interactive lessons, provide detailed feedback, and answer questions with factual accuracy.

Enhancing scientific research

The integrated frameworks provide an ability to analyze vast amounts of scientific data, identify patterns, and even suggest new hypotheses. The combination has the potential to accelerate scientific research across various fields.



Intelligent customer service

With useful knowledge representations of KGs, LLMs can generate personalized and more accurate support. It will also enhance their ability to troubleshoot issues and offer improved recommendations, providing an intelligent customer experience to the users of any enterprise.

Thus, the integration of knowledge graphs and LLMs has the potential to boost the development of AI-powered tasks and transform the field of NLP.

March 28, 2024

With data being a valuable instrument in a modern approach to business, its impact on logistics and supply chain management can hardly be overestimated. As enterprises are trying hard to earn more customers and hence gain a competitive edge over their rivals, the need to stay current with global changes causes massive adoption of high technologies within the industry.

In this way, logistics companies are transforming their traditional approach to supply chain management (SCM) based on the power of data. As the supply system is a set of complex stages from the purchase of initial material to delivering a ready-made product to final consumers, it requires effective management and control.

Hence, by integrating data analytics in SCM, companies can analyze figures and make informed business decisions. This leads to optimization and boosting efficiency of crucial supply chain operations and enhanced customer experience.


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Besides, data analytics solutions contribute to increased cost effectiveness which is proved by the following estimates: businesses with optimized supply chains reduce supply chain expenditures by 15% and make cash-to-cash cycles 3 times faster. In this event, 79% of companies with effective supply chains have reported above-average revenue growth, as a 2023 survey by Deloitte states.

Thus, this article explains the role of data analytics in optimizing supply chain logistics.


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Data analytics basics for logistics and supply chain management

Analytics, being a core component of supply chain management, processes and assesses vast amounts of data generated within the frames of the entire supply cycle. It collects information from suppliers, dealers, deliverers, warehouses, etc.

As a result of such an analysis, companies gain relevant information and leverage it for making more informed decisions.  The function of real-time performance monitoring allows companies to get insights into order processing time, delivery, and inventory.

The opportunity to observe the relations between these processes helps to identify inefficiencies, detect or foresee any risks, and then take measures for their troubleshooting. 

In this way, one of the most obvious benefits of analytics is its ability to furnish personnel with information that can be applied to optimizing operations, reducing costs, and streamlining the supply network.


Benefits of Data Analytics on Supply Chain Management
Benefits of data analytics on supply chain management


Predictive analytics for demand forecasting

Smart analytics tools leveraged in SCM are capable of predicting customer demand based on previous sales data, seasonality, market trends, various economic factors, and others. Estimating future demand for a product or service helps companies regulate procurement, production, and inventories.

Enabling industries with more accurate planning and predictive analytics contributes to risk mitigation and reduction of inventory costs. In this way, AI-powered forecasting offers a way to minimize the need for manual labor and maximize the operational efficiency of business. 

Optimization of inventory management

The ability to respond to ever-changing consumer demands requires having the right products in the right quantity at the right time. Thus, data analytics give insights into the amount of inventory necessary to satisfy the demand without overstocking, outstocking, and product recalling which contributes to reduced operational costs. 

What’s more, smart tools integrated into the supply chain identify the most suitable locations and facilities for storing products. With their help, orders can be filled much more quickly as the goods are sent to the customer’s address from the closest warehouse.


Read more about decoding the data storage debate


Supplier Relationship Management (SRM)

Well-established cooperation with vendors and suppliers is crucial for the seamless operation of a supply chain. For this reason, businesses adopt SRM models which include a variety of processes from selecting vendors to their performance management and aim at fostering mutually beneficial relationships with them.

Applying analytics in SRM, businesses obtain data that allows assessing the performance and reliability of their suppliers as well as tracing potential risks. The above capabilities of analytics give a detailed overview of all activities associated with SRM and help to identify problem zones to be improved. 

Enhancing customer experience through analytics

Understanding the buying patterns and preferences of customers helps businesses in the choice of their marketing strategies and product offerings. When applied correctly, consumer experience analytics indicate any signals of dissatisfaction and help businesses take necessary steps to minimize buyer churn.


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It reflects the way customers interact with products or services and therefore allows for building the right strategy to ensure a personalized experience for buying individuals. Knowledge of customer behavior obtained through smart analysis helps to increase customer loyalty and satisfaction, boost repurchasing rates, and as a result, generate revenue growth. 

Data security and privacy in supply chain analytics

Growing reliance on a data-driven approach to SCM has caused the necessity to enhance the security and protection of sensitive information. Cyber threats and risks of data breaches represent a serious danger for supply chains that contain customer details, product information, pricing, intellectual property, and other confidential data.

To avoid any intervention and unauthorized exploitation of this data, one should implement security measures (encryption, access controls) and maintain their SCM software regularly.




With high technologies evolving day by day, data analytics will become even more important in optimizing supply chain logistics. As seen from the above, integrating this solution in SCM opens up a new way for businesses to perform in the global market. Companies leveraging the power of data in basic and complex operations enhance their efficiency, reduce costs, minimize risks, and ensure an exceptional quality of service to their customers.  

March 26, 2024

MySQL is a popular database management system that is used globally and across different domains. In this article, you will learn more about how it works, where it is used, and how to work with MySQL.

What is MySQL?

MySQL is widely used by web developers and large companies for storing and managing data. It is one of the most popular database management systems (DBMS) globally that supports all major operating systems: Linux, macOS, and Windows.

Databases are stored on a server, which is typically a remote computer or a cloud server. When you need data, you send a query to the server using your computer, or client, and you receive the information. To manage queries, a special language called Structured Query Language (SQL) is used.


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Imagine you have an online clothing store. You need to keep track of all your products, customers, and sales. MySQL can be used for this purpose.

In the DBMS, separate tables are created for products, customers, and sales. The first table stores information about each product, such as its name, price, and available quantity. The second table contains names, contact information, and payment details. The third table holds information about customers who purchased the product, sale dates, and purchased item names.

If you want to know how many sales occurred and who bought products last month, you write a query, send it to the server, and get a list of the relevant data.


Understand the database dilemma of SQL vs NoSQL


MySQL enables storing and processing information, especially crucial when dealing with large amounts of data. A small store with one seller may record everything in an Excel spreadsheet. Still, for a large network with hundreds of daily purchases, this approach becomes inconvenient.

However, MySQL is not only used in retail but in any context where data is involved.

What is SQL?

To communicate with a database, you need to know its language – SQL, which stands for Structured Query Language. Each query must follow a specific structure for the database to understand you.

At the beginning of the query, there is an action – delete, select, add, followed by a keyword indicating from which table to extract information. Further, there is a statement explaining which details and from which cell of the table to retrieve. The query may also include a condition at the end: for example, the action will be performed only under specific circumstances.


Understanding the general query structure for a database management system
Understanding the general query structure for a database management system


Queries are entered through the terminal – for this, you need to download a specific program. With its help, you can create and modify tables, link them, add and delete data, and find what you need.

Here’s an SQL crash course for a beginner to explore.



What is MySQL used for?

With MySQL, you can store any type of data: text, numbers, images, audio and video files, and graphics. Thanks to the system’s performance, even very large volumes of data can be stored, and everything will still function normally. Obtaining the required information can be quick if you know how to use SQL.

MySQL addresses the issue of multiple users when several users are modifying data in the database simultaneously, and only one version is saved. When a user connects to MySQL, the server creates a new entry for them in the metadata table, containing information about the connection state, including the current transaction, if any. This ensures that the data is kept up-to-date.


Here’s a guide to understanding different types of databases


Additionally, MySQL uses a system of locks to control access to data. When a user tries to access a table, MySQL checks if the table is locked by another user. If the table is locked, the user must wait until the lock is released. This prevents data from being overwritten, and nothing is lost.

Different permissions allow server administrators to manage users’ access to various functions and data. For example, access can be granted only to the data necessary for work, enhancing data storage security.

Who uses MySQL?

Here are a few examples of how large companies use MySQL:

Tesla uses MySQL to store and process data about its cars, including battery status, mileage, speed, and other parameters. MySQL allows Tesla to quickly access this data and analyze it to improve the performance and safety of their cars.

Netflix stores data about its users, such as viewing history, preferences, and recommendations, using a DBMS. This tool helps the company improve its recommendations and personalize content.

PayPal utilizes MySQL to collect and store transaction information, using this data to enhance the security and efficiency of payments.

Essentially, MySQL can be applied in any application or web service, be it an online cinema, a store, a blog, or social network.

Advantages of MySQL

These advantages make MySQL one of the most popular and widely used database management systems in the world:

  • It is free to use; MySQL has open-source code.
  • No need to worry about performance; MySQL is optimized to handle a large number of queries.
  • No need to worry about data; MySQL supports backup and restoration mechanisms that ensure data integrity.
  • Applications are easily expandable; MySQL supports various data storage types, table engines (such as InnoDB, and MyISAM), and other features that enable developers to create complex applications.

How is the MySQL database management system structured?

MySQL consists of several components, each serving specific functions:

  • The main component manages all database operations. The MySQL server receives requests from clients, processes them, performs necessary data operations, and returns the results to clients.
  • Mysqld Service. A process running on the server responsible for managing databases. It accepts requests from clients, processes them, performs read and write data operations, manages transactions, and ensures data security.
  • Data Storage. MySQL uses various types of data storage, such as InnoDB, MyISAM, MEMORY, and others, each with specific features designed for certain data types or tasks.
  • Client Applications. Various client applications, such as MySQL Workbench, phpMyAdmin, the MySQL command-line interface, and others, are used to work with MySQL. These applications allow administrators and developers to create, modify, and manage databases through graphical or text interfaces, such as the terminal.


Explore the debate between traditional vs vector databases


How to work with MySQL

Let’s go through using the database management system step by step.


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  • Installation and setup of MySQL
    To work with MySQL, you need to install the database server on your computer or use online hosting. You can download the program from the official Oracle website, especially if you are working on significant projects. However, for educational purposes, I will be using MySQL in the browser.


Installation and setup of MySQL
Installation and setup of MySQL


  • Creating a database
    Next, you can create databases and tables, add data, execute queries to retrieve information, and much more using SQL. Let’s create a table for friends from a TV show and their professions.


Creating a database
Creating a database


  • Adding data
    Populate the tables with data using INSERT statements or import data from files.


Adding data
Adding data


  • Retrieving data
    Use the SELECT statement to extract data from the table. You can perform various queries, filter data, sort, and group results.


Retrieving data
Retrieving data


  • Updating and deleting data
    Use UPDATE and DELETE statements to modify and remove data from the table.


Updating and deleting data
Updating and deleting data


  • Run the program by clicking “Run.”
    We see the program’s results on the right. We requested information about a person working in PR and received the answer – Chandler.


Run the program
Run the program


This is just a general overview of the process of working with MySQL. For more detailed information and to learn about SQL queries, functions, and MySQL capabilities, refer to the MySQL documentation.

Here’s an overview of MySQL, tools you need to interface with the newly set up RDBMS, and a few datasets that can be used to populate a small testing environment.


March 25, 2024

Natural language processing (NLP) and large language models (LLMs) have been revolutionized with the introduction of transformer models. These refer to a type of neural network architecture that excels at tasks involving sequences.

While we have talked about the details of a typical transformer architecture, in this blog we will explore the different types of the models.

How to categorize transformer models?

Transformers ensure the efficiency of LLMs in processing information. Their role is critical to ensure improved accuracy, faster training on data, and wider applicability. Hence, it is important to understand the different model types available to choose the right one for your needs.


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However, before we delve into the many types of transformer models, it is important to understand the basis of their classification.

Classification by transformer architecture

The most fundamental categorization of transformer models is done based on their architecture. The variations are designed to perform specific tasks or cater to the limitations of the base architecture. The very common model types under this category include encoder-only, decoder-only, and encoder-decoder transformers.

Categorization based on pre-training approaches

While architecture is a basic component of consideration, the training techniques are equally crucial components for transformers. Pre-training approaches refer to the techniques used to train a transformer on a general dataset before finetuning it to perform specific tasks.

Some common approaches that define classification under this category include Masked Language Models (MLMs), autoregressive models, and conditional transformers.

This presents a general outlook on classifying transformer models. While we now know the types present under each broader category, let’s dig deeper into each transformer model type.


Read in detail about transformer architectures


Architecture-based classification


Architecture of transformer models
The general architecture of transformer models


Encoder-only transformer

As the name suggests, this architectural type uses only the encoder part of the transformer, focusing on encoding the input sequence. For this model type, understanding the input sequence is crucial while generating an output sequence is not required.

Some common applications of an encoder-only transformer include:

Text classification

It is focused on classifying the input data based on defined parameters. It is often used in email spam filters to categorize incoming emails. The transformer model can also train over the patterns for effective filtration of unwanted messages.

Sentimental analysis

This feature makes it an appropriate choice for social media companies to analyze customer feedback and their emotion toward a service or product. It provides useful data insights, leading to the creation of effective strategies to enhance customer satisfaction.

Anomaly detection

It is particularly useful for finance companies. The analysis of financial transactions allows the timely detection of anomalies. Hence, possible fraudulent activities can be addressed promptly.

Other uses of an encoder-only transformer include question-answering, speech recognition, and image captioning.

Decoder-only transformer

It is a less common type of transformer model that uses only the decoder component to generate text sequences based on input prompts. The self-attention mechanism allows the model to focus on previously generated outputs in the sequence, enabling it to refine the output and create more contextually aware results.

Some common uses of decoder-only transformers include:

Text summarization

It can iteratively generate textual summaries of the input, focusing on including the important aspects of information.

Text generation

It builds on a provided prompt to generate relevant textual outputs. The results cover a diverse range of content types, like poems, codes, and snippets. It is capable of iterating the process to create connected and improved responses.


It is useful to handle conversational interactions via chatbots. The decoder can also consider previous conversations to formulate relevant responses.


Explore the role of attention mechanism in transformers


Encoder-decoder Transformer

This is a classic architectural type of transformer, efficiently handling sequence-to-sequence tasks, where you need to transform one type of sequence (like text) into another (like a translation or summary). An encoder processes the input sequence while a decoder is used to generate an output sequence.

Some common uses of an encoder-decoder transformer include:

Machine translation

Since the sequence is important at both the input and output, it makes this transformer model a useful tool for translation. It also considers contextual references and relationships between words in both languages.

Text summarization

While this use overlaps with that of a decoder-only transformer, text summarization differs from an encoder-decoder transformer due to its focus on the input sequence. It enables the creation of summaries that focus on relevant aspects of the text highlighted in an input prompt.


It is important to understand the question before providing a relevant answer. An encoder-decoder transformer allows this focus on both ends of the communication, ensuring each question is understood and answered appropriately.

This concludes our exploration of architecture-based transformer models. Let’s explore the classification from the lens of pre-training approaches.

Categorization based on pre-training approaches

While the architectural differences provide a basis for transformer types, the models can be further classified based on their techniques of pre-training.

Let’s explore the various transformer models segregated based on pre-training approaches.

Masked Language Models (MLMs)

Models with this pre-training approach are usually encoder-only in architecture. They are trained to predict a masked word in a sentence based on the contextual information of the surrounding words. The training enables these model types to become efficient in understanding language relationships.

Some common MLM applications are:

Boosting downstream NLP tasks

MLMs train on massive datasets, enabling the models to develop a strong understanding of language context and relationships between words. This knowledge enables MLM models to contribute and excel in diverse NLP applications.

General-purpose NLP tool

The enhanced learning, knowledge, and adaptability of MLMs make them a part of multiple NLP applications. Developers leverage this versatility of pre-trained MLMs to build a basis for different NLP tools.

Efficient NLP development

The pre-trained foundation of MLMs reduces the time and resources needed for the deployment of NLP applications. It promotes innovation, faster development, and efficiency.


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Autoregressive models

Typically built using a decoder-only architecture, this pre-training model is used to generate sequences iteratively. It can predict the next word based on the previous one in the text you have written. Some common uses of autoregressive models include:

Text generation

The iterative prediction from the model enables it to generate different text formats. From codes and poems to musical pieces, it can create all while iteratively refining the output as well.


The model can also be utilized in a conversational environment, creating engaging and contextually relevant responses,

Machine translation

While encoder-decoder models are commonly used for translation tasks, some languages with complex grammatical structures are supported by autoregressive models.

Conditional transformer

This transformer model incorporates the additional information of a condition along with the main input sequence. It enables the model to generate highly specific outputs based on particular conditions, ensuring more personalized results.

Some uses of conditional transformers include:

Machine translation with adaptation

The conditional aspect enables the model to set the target language as a condition. It ensures better adjustment of the model to the target language’s style and characteristics.

Summarization with constraints

Additional information allows the model to generate summaries of textual inputs based on particular conditions.

Speech recognition with constraints

With the consideration of additional factors like speaker ID or background noise, the recognition process enhances to produce improved results.

Future of transformer model types

While numerous transformer model variations are available, the ongoing research promises their further exploration and growth. Some major points of further development will focus on efficiency, specialization for various tasks, and integration of transformers with other AI techniques.

Transformers can also play a crucial role in the field of human-computer interaction with their enhanced capabilities. The growth of transformers will definitely impact the future of AI. However, it is important to understand the uses of each variation of a transformer model before you choose the one that fits your requirements.

March 23, 2024