LLMs MasterClass: Last Day for Early-Bird Price

Today is the last day to get early bird pricing (25%) for the Train, Fine-Tune, and Deploy Large Language Models Masterclass ! Additionally, members of the AiEdge Newsletter get an additional 20% discount by applying the following coupon: NEWSLETTER. Make sure to register before it is too late.

You can get an additional 20% off if you enroll in the ML fundamentals BootCamp as well: Bootcamps Bundle

The BootCamp will start on August 15th, 2024. This is going to be 6 weeks of intense hands-on learning to become an expert in building LLM applications.

What you are going to build in the projects (May be subject to changes)

This bootcamp is hands-on, which means that it is meant to prepare you for the job. It is going to be more practical than theoretical, and we are going to implement the following in the projects:

Project 1: Implementing from scratch The sparse attention mechanisms, SliGLU, RMSNorm, MoE, and Rope embedding in PyTorch

Project 2: Fine-tuning an LLM with PPO vs DPO vs ORPO using the PEFT package.

Project 3: Train an LLM in a distributed manner with the Accelerate package in AWS SageMaker with the Zero Redundancy Optimizer Strategy.

Project 4: Fine-tuning a model with QLoRA to increase the context size.

Project 5: Deploying a scalable LLM application API with streaming, KV-caching, Continuous batching, and text generation layer capabilities.

Project 6: Deploying an RAG application using LangChain, FastAPI, and LangServe.

Who is this Bootcamp for?

This Bootcamp is meant for Engineers with experience in Data Science or Machine Learning Engineering who want to upgrade their skills in Large Language Modeling. This Bootcamp is meant for Engineers with some experience in Data Science or Machine Learning Engineering who want to upgrade their skills in Large Language Modeling. This Bootcamp is not meant to be easy, but I can promise you that your understanding of LLMs will be on a completely different level!

Prerequisites

• Prior experience or knowledge of Machine Learning - at least 6 months. I expect people to feel comfortable with the concepts developed in the Machine Learning Fundamental Bootcamp .
• Proficiency in Python - at least 1 year experience.

What is included!

• 40+ hours of recorded lectures
• 6 hands-on projects
• Homework support
• Certification upon graduation
• Access to our online community
• Lifetime access to course content

The Schedule

We are going to meet every Thursday and Friday between 9 am and 12 pm PST starting August 15th.

6 Weeks of Intense Learning!

The Transformer Architecture (1 week)

The Transformer is the fundamental Neural Network architecture that enabled the evolution of Large Language Models as we know them now.

• The Self-Attention Mechanism
• The Multihead attention
• The encoder-decoder architecture
• The position embedding
• The layer-normalization
• The position-wise feed-forward network
• The cross-attention layer
• The language modeling head

Training LLMs to Follow Instruction (1 week)

GhatGPT, Claude, or Gemini are LLMs trained to follow human instructions. We are going to learn how those are trained from scratch:

• The Causal Language Modeling Pretraining Step
• The Supervised Learning Fine-Tuning Step
• The Reinforcement Learning Fine-Tuning Step
• Implementing those Steps with HuggingFace

How to Scale Model Training (1 week)

More than ever, we need efficient hardware to accelerate the training process. We are going to explore the strategy of distributing training computations across multiple GPUs for different parallelism strategies:

• CPU vs GPU vs TPU
• The GPU Architecture
• Distributed Training
• Data Parallelism
• Model Parallelism
• Zero Redundancy Optimizer Strategy

How to Fine-Tune LLMs (1 week)

Fine-tuning a model means we continue the training on a specialized dataset for a specialized learning task. We are going to look at the different strategies to fine-tune LLMs:

• The different fine-tuning learning tasks
• Catastrophic forgetting
• LoRA Adapters
• QLoRA

How to Deploy LLMs (1 week)

The most important part of a machine learning model development is the deployment! A model that is not in production is a model that is costing money instead of generating money for the company. We are going to explore the different strategies to deploy LLMs:

• The Deployment Strategies
• Multi-LoRA
• The Text Generation Layer
• Streaming Applications
• Continuous Batching
• KV-Caching
• The Paged-Attention and vLLM

Building the Application Layer (1 week)

A deployed LLM on its own is not really useful. We are going to look at how we can build an agentic application on top of the model with LangChain:

• Implementing a Retriever Augmented Generation (RAG) pipeline with LangChain
• Optimizing the RAG pipeline
• Serving the pipeline with LangServe and FastAPI

Don’t hesitate to send me an email if you have more questions: [email protected] .

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