AI Courses

Spec-Driven Development with Agentic Coding Assistants

[INTENRAL ONLY] Draft Mode in Internal Tool

Gemini CLI: Code & Create with an Open-Source Agent

Build real-world applications from the command line using Gemini CLI, Google's open-source agentic coding assistant that coordinates local tools and cloud services to automate coding and creative workflows.

Document AI: From OCR to Agentic Doc Extraction

Build agentic systems to parse documents and extract information grounded in visual components like charts, tables, and forms.

Nvidia’s NeMo Agent Toolkit: Making Agents Reliable

Turn proof-of-concept agent demos into production-ready systems using observability, evaluation, and deployment tools from Nvidia's NeMo Agent Toolkit.

Multi-vector Image Retrieval

Build advanced retrieval systems that represent images with multiple vectors, enabling fine-grained matching between text queries and visual content for accurate multi-modal search.

Building Coding Agents with Tool Execution

Build AI agents that write and execute code to accomplish tasks, running safely in sandboxed cloud environments that protect your systems from untrusted code.

Semantic Caching for AI Agents

Speed up and reduce the costs of your AI agents by implementing semantic caching that reuses responses based on meaning rather than exact text.

Jupyter AI: AI Coding in Notebooks

Learn to code with AI in Jupyter notebooks. Use Jupyter AI to generate code, get explanations, and analyze data.

Governing AI Agents

Integrate data governance into your agent's workflow to ensure it handles data safely, securely, and accurately.

Building Live Voice Agents with Google’s ADK

Build real-time voice AI agents, from simple to multi-agent podcast systems, using Google’s Agent Development Kit.

Build AI Apps with MCP Server: Working with Box Files

Build an LLM app that uses tools from the Box MCP server to discover Box files and extract text from them. Transform it into a multi-agent system that communicates using A2A.

Knowledge Graphs for AI Agent API Discovery

Construct a knowledge graph and use it to enable your AI agent to find and call the right APIs in the right order.

digitalocean-test

Ryan Test Labs (TBD)

Agentic Knowledge Graph Construction

Build a multi-agent system that plans, designs, and constructs a knowledge graph.

Claude Code: A Highly Agentic Coding Assistant

Explore, build, and refine codebases with Claude Code.

Pydantic for LLM Workflows

Build reliable LLM applications with structured outputs and validated data using Pydantic.

Test for Aider

ACP: Agent Communication Protocol

Build agents that communicate and collaborate across different frameworks using ACP.

Building with Llama 4

Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools.

DSPy: Build and Optimize Agentic Apps

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

MCP: Build Rich-Context AI Apps with Anthropic

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

Building AI Voice Agents for Production

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

Getting Structured LLM Output

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

Building Code Agents with Hugging Face smolagents

Build agents that write and execute code to perform complex tasks, using Hugging Face’s smolagents.

LLMs as Operating Systems: Agent Memory

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

Building AI Browser Agents

Build agents that navigate and interact with websites, and learn how to make them more reliable.

Vibe Coding 101 with Replit

Design, build, and deploy apps with an AI coding agent in an integrated web development environment.

Long-Term Agentic Memory With LangGraph

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

Event-Driven Agentic Document Workflows

Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.

Build Apps with Windsurf’s AI Coding Agents

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Attention in Transformers: Concepts and Code in PyTorch

Understand and implement the attention mechanism, a key element of transformer-based LLMs, using PyTorch.

Evaluating AI Agents

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

How Transformer LLMs Work

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

Building toward Computer Use with Anthropic

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Functions Tools and Agents with LangChain - Extra

Build Long-Context AI Apps with Jamba

Build LLM apps that can process very long documents using the Jamba model

Reasoning with o1

Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.

Collaborative Writing and Coding with OpenAI Canvas

Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.

Building an AI-Powered Game

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

Safe and reliable AI via guardrails

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

Practical Multi AI Agents and Advanced Use Cases with crewAI

Build agents that collaborate to solve complex business tasks.

Serverless Agentic Workflows with Amazon Bedrock

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

Introducing Multimodal Llama 3.2

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Retrieval Optimization: From Tokenization to Vector Quantization

Build faster and more relevant vector search for your LLM applications

Large Multimodal Model Prompting with Gemini

Learn best practices for multimodal prompting using Google’s Gemini model.

Building AI Applications With Haystack

Learn a flexible framework to build a variety of complex AI applications.

Improving Accuracy of LLM Applications

Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.

Embedding Models: from Architecture to Implementation

Learn how to build embedding models and how to create effective semantic retrieval systems.

Federated Fine-tuning of LLMs with Private Data

Learn how to securely fine-tune large language models (LLMs) with private data using federated methods, enhancing data privacy, minimizing risks of data leakage, and optimizing efficiency through Parameter-Efficient Fine-Tuning (PEFT) and Differential Privacy.

Intro to Federated Learning

Build and fine-tune LLMs across distributed data using a federated learning framework for better privacy.

Pretraining LLMs

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

Prompt Compression and Query Optimization

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

Carbon Aware Computing for Gen AI developers

Train your machine learning models using cleaner energy sources.

Function-calling and data extraction with LLMs

Learn to apply function-calling to expand LLM and agent application capabilities.

Building Your Own Database Agent

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

AI Agents in LangGraph

Build agentic AI workflows using LangChain's LangGraph and Tavily's agentic search.

AI Agentic Design Patterns with AutoGen

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

Introduction to on-device AI

Deploy AI for edge devices and smartphones. Learn model conversion, quantization, and how to modify for deployment on diverse devices.

Multi AI Agent Systems with crewAI

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Building Multimodal Search and RAG

Build smarter search and RAG applications for multimodal retrieval and generation.

Building Agentic RAG with Llamaindex

Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.

Quantization in Depth

Customize model compression with advanced quantization techniques. Try out different variants of Linear Quantization, including symmetric vs. asymmetric mode, and different granularities.

Prompt Engineering for Vision Models

Learn prompt engineering for vision models using Stable Diffusion, and advanced techniques like object detection and in-painting.

Getting Started with Mistral

Explore Mistral's open-source and commercial models, and leverage Mistral's JSON mode to generate structured LLM responses. Use Mistral's API to call user-defined functions for enhanced LLM capabilities.

Quantization Fundamentals

Learn how to quantize any open-source model. Learn to compress models with the Hugging Face Transformers library and the Quanto library.

Preprocessing Unstructured Data for LLM Applications

Improve your RAG system to retrieve diverse data types. Learn to extract and normalize content from a wide variety of document types, such as PDFs, PowerPoints, and HTML files.

Red Teaming LLM Applications

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

JavaScript RAG Web Apps with LlamaIndex

Build a full-stack web application that uses RAG capabilities to chat with your data. Learn to build a RAG application in JavaScript, using an intelligent agent to answer queries.

Efficiently Serving LLMs

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.

Knowledge Graphs for RAG

Learn how to build and use knowledge graph systems to improve your retrieval augmented generation applications. Use Neo4j's query language Cypher to manage and retrieve data.

Open Source Models with Hugging Face

Learn how to easily build AI applications using open-source models and Hugging Face tools. Find and filter open-source models on Hugging Face Hub.

Prompt Engineering with Llama 2 & 3

Learn best practices for prompting and selecting among Meta Llama 2 & 3 models. Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models.

Serverless LLM Amazon Bedrock

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

Building Applications with Vector Databases

Learn to build six applications powered by vector databases, including semantic search, retrieval augmented generation (RAG), and anomaly detection.

Automated Testing for LLMOps

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

LLMOps

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

Build LLM Apps with LangChain.js

Expand your toolkit with LangChain.js, a JavaScript framework for building with LLMs. Understand the fundamentals of using LangChain to orchestrate and chain modules.

Advanced Retrieval for AI with Chroma

Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.

Reinforcement Learning From Human Feedback

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

Building and Evaluating Advanced RAG

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

Quality and Safety for LLM Applications

Learn how to evaluate the safety and security of your LLM applications and protect against risks. Monitor and enhance security measures to safeguard your apps.

Vector Databases: from Embeddings to Applications

Design and execute real-world applications of vector databases. Build efficient, practical applications, including hybrid and multilingual searches.

Functions, Tools and Agents with LangChain

Learn about the latest advancements in LLM APIs and use LangChain Expression Language (LCEL) to compose and customize chains and agents.

Pair Programing with a Large Language Model

Learn how to prompt an LLM to help improve, debug, understand, and document your code. Use LLMs to simplify your code and enhance productivity.

Understanding and Applying Text Embeddings with Vertex AI

Learn how to accelerate the application development process with text embeddings for sentence and paragraph meaning.

Developing Business Applications using LLMs via Semantic Kernel

Learn Microsoft's open source orchestrator, Semantic Kernel and use LLM building blocks such as memory, connectors, chains and planners in your apps.

Finetuning Large Language Models

Large Language Models with Semantic Search

Learn to use LLMs to enhance search and summarize results, using Cohere Rerank and embeddings for dense retrieval.

Evaluating and Debugging Generative AI

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

Building Machine Learning Demos with Gradio

Create and demo machine learning applications quickly. Share your app with teammates and beta testers on Hugging Face Spaces.

LangChain Chat with your data

Create a chatbot with LangChain to interface with your private data and documents. Learn from LangChain creator, Harrison Chase.

Building Systems with the ChatGPT API

Learn to break down complex tasks, automate workflows, chain LLM calls, and get better outputs from LLMs. Evaluate LLM inputs and outputs for safety and relevance.

How Diffusion Models Work

Learn and build diffusion models from the ground up, understanding each step. Learn about diffusion models in use today and implement algorithms to speed up sampling.

LangChain for LLM Application Development

Use the powerful and extensible LangChain framework, using prompts, parsing, memory, chains, question answering, and agents.

ChatGPT Prompt Engineering for Developers

Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding.