Large Language Models Bootcamp
NEW
. 5 DAYS . 40 HOURS .
IN-PERSON
Comprehensive, hands-on curriculum to give you a headstart in building Large Language Model Applications.
4.95 · 640+ reviewsSoftware subscriptions and cloud services credit up to USD $500.
Breakfast, lunch and beverages daily.
Networking dinner.
Course material and access to coding labs, Jupyter notebooks, and hundreds of learning resources for 1 year.
Pre-trained Large Language Models like ChatGPT offer impressive capabilities but they cannot be used in scenarios where the underlying data is proprietary and requires industry-specific knowledge. Businesses are rushing to build custom LLM applications that offer enhanced performance, control, customization and most importantly, competitive advantage.
This bootcamp offers a comprehensive introduction to get started with building a ChatGPT on your own data. By the end of the bootcamp, you will be capable of building LLM-powered applications on any dataset of your choice.
In collaboration with our delivery partners
Anyone interested in getting a headstart by getting a hands-on experience with building LLM applications.
Data professionals who want to supercharge their data skills using cutting-edge generative AI tools and techniques.
Product leaders at enterprises or startups seeking to leverage LLMs to enhance their products, processes and services.
4.95 · 640+ reviewsSoftware subscriptions and cloud services credit up to USD $500.
Breakfast, lunch and beverages daily.
Networking dinner.
Course material and access to coding labs, Jupyter notebooks, and hundreds of learning resources for 1 year.
Dive into machine learning with a focus on large language models. Learn the differences between traditional programming and ML, understand neural network structures, and explore the evolution and importance of foundation models
Quick overview of generative AI, LLM's, and foundation models. Learn more about how transformers and attention mechanism works behind text and image based models.
In this module, we will be reviewing how embeddings have evolved from the simplest one-hot encoding approach to more recent semantic embeddings approaches. The module will go over the following topics:
Hands-on Exercise
Dive into the world of large language models, discovering the potent mix of text embeddings, attention mechanisms, and the game-changing transformer model architecture.
Attention mechanism and transformer models Hands-on Exercise
In collaboration with 
Learn about efficient vector storage and retrieval with vector database, indexing techniques, retrieval methods, and hands-on exercises.
In this module we will understand the common use cases of large language models and fundamental building blocks of such applications. Learners will be introduced to the following topics at a very high level without going into the technical details:
Understand how semantic search overcomes the fundamental limitation in lexical search i.e. lack of semantic . Learn how to use embeddings and similarity in order to build a semantic search model.
Unleash your creativity and efficiency with prompt engineering. Seamlessly prompt models, control outputs, and generate captivating content across various domains and tasks.
In collaboration with 
Discover the ins and outs of fine-tuning foundation language models (LLMs) through theory discussions, exploring rationale, limitations, and Parameter Efficient Fine Tuning.
Explore the necessity of orchestration frameworks, tackling issues like foundation model retraining, token limits, data source connectivity, and boilerplate code. Discover popular frameworks, their creators, and open source availability.
In collaboration with 
Build LLM Apps using LangChain. Learn about LangChain's key components such as Models, Prompts, Parsers, Memory, Chains, and Question-Answering. Get hands-on evaluation experience.
Use LLMs to make decisions about what to do next. Enable these decisions with tools. In this module, we’ll talk about agents. We’ll learn what they are, how they work, and how to use them within the LangChain library to superpower our LLMs.
In collaboration with 
LLMOps encompasses the practices, techniques and tools used for the operational management of large language models in production environments. LLMs offer tremendous business value, humans are involved in all stages of the lifecycle of an LLM from acquisition of data to interpretation of insights. In this module we will learn about the following:
On the last day of the bootcamp, the learners will apply the concepts and techniques learned during the bootcamp to build an LLM application. Learners will choose to implement one of the following:
Attendees will receive the following: At the end of the bootcamp, you'll leave with a fully functional LLM application deployed in either Streamlit or Hugging Face cloud. Showcase your achievements and leverage the power of AI to transform your industry, gaining a competitive edge and staying ahead of the curve.
Yes.
You will leave the bootcamp with your own fully functional LLM application.
Yes. You will receive a verified certificate from The University of New Mexico carrying 7 CEUs.
Yes, you can find out more here.
Yes, you can. You will also have the option to choose from the various datasets provided for the end-to-end project.
Yes, you will be provided with the pre-requisite material on our learning platform before the bootcamp.
