Learn advanced RAG retrieval methods like sentence-window and auto-merging that outperform baselines, and evaluate and iterate on your pipeline's performance.

Learn how to deploy an LLM-based application into production using serverless technology. Learn to prompt and customize LLM responses with Amazon Bedrock.

Interact with tabular data and SQL databases using natural language, enabling more efficient and accessible data analysis.

Train your machine learning models using cleaner energy sources.

Optimize the efficiency, security, query processing speed, and cost of your RAG applications.

Learn the essential steps to pretrain a large language model from scratch.

Move your LLM-powered applications beyond proof-of-concept and into production with the added control of guardrails.

Learn to build with LLMs by creating a fun interactive game from scratch.

Understand the transformer architecture that powers LLMs to use them more effectively.

Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.

Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management.

Learn how to generate structured outputs to power production-ready LLM software applications.

Build responsive, scalable, and human-like AI voice applications.

Build AI apps that access tools, data, and prompts using the Model Context Protocol.

Improve LLM reasoning with reinforcement fine-tuning and reward functions.

Build, debug, and optimize AI agents using DSPy and MLflow.

Turn your GenAI prototype into an automated pipeline using Apache Airflow