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

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

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

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

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

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

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

Build systems with MemGPT agents that can autonomously manage their memory.

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

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

Train your machine learning models using cleaner energy sources.

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

Learn how to make safer LLM apps through red teaming. Learn to identify and evaluate vulnerabilities in large language model (LLM) applications.

Understand how LLMs predict the next token and how techniques like KV caching can speed up text generation. Write code to serve LLM applications efficiently to multiple users.

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

Learn how to create an automated CI pipeline to evaluate your LLM applications on every change, for faster and safer development.

Learn LLMOps best practices as you design and automate steps to fine-tune and deploy an LLM for a specific task.

Get an introduction to tuning and evaluating LLMs using Reinforcement Learning from Human Feedback (RLHF) and fine-tune the Llama 2 model.

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

Learn MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.