Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

Repository for the oreilly live training course: "Getting Started with Llama2"

Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!

Hands-on with AWS in 3 weeks

This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), ar…

Metrics, Benchmarks, and Practical Tools for Assessing Large Language Models

This is an example of a Containerized Flask Application that can deploy to many target environments including: AWS, GCP and Azure.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Examples used in the AI-Tools Course

Official code repo for the O'Reilly Book - "Hands-On Large Language Models"

LangGraph solution template for MCP

Build resilient language agents as graphs.

My Landing page

Repository for my OReilly Live-Training course on "Getting Started with LangGraph"

How to professionally package your ML model for production.

To show the usefulness of data engineering and ML pipelines.

Architecting AWS with Terraform, published by Packt

Code Repository for Machine Learning, Data Science and Generative AI with Python, Published by Packt

Generative AI with Python and PyTorch , Second Edition - Published by Packt

Resources for the O'Reilly Online Training "Machine Learning from Scratch"

Modern Generative AI with ChatGPT and OpenAI Models, published by Packt

Python Machine Learning by Example, Fourth Edition

This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face model…

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices