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From a local model to production

An AI engineering curriculum of 42 lessons and 2 capstones.

M00/7 lessons

Build Your First AI App

Build a local-first AI assistant with streaming, memory, and a real backend

Welcome to MLExpert
Setup Your Environment
Run AI Locally with OllamaFree
Prompt Engineering for Engineers
LangChain QuickstartFree
Streaming API with FastAPI
NeuroMind - Chatbot with MemoryCapstone

M01/10 lessons

RAG and Context Engineering

Build document AI that shows sources, handles messy context, and can be evaluated

Build a Cache-Augmented Generation (CAG) System
RAG from First Principles
Document Processing
Chunking Strategies
Embeddings and Vector Databases
Hybrid Retrieval
LangGraph QuickstartFree
Observability and Tracing
The RAG Evaluation Triad
FinVault - RAG for Financial DocumentsCapstone

M02/8 lessons

AI Agents and Workflows

Build agents and workflows with tool use, recovery loops, and failure traces

Workflows vs AgentsFree
Vectorless RAG
Self-Correcting Agent
Build an MCP Agent
Plan and Execute Agent
Thinking and Acting - Build an AI Agent
Agentic RAG - Building an AI Financial Analyst Team
Evaluating Agentic Systems

M03/8 lessons

AI Systems Engineering

Turn prototypes into systems with evals, datasets, fine-tuning, and LLMOps

The AI Engineer Toolkit - APIs, structured output, tools
Connect AI to External Systems - Model Context Protocol
Build Your Own Dataset with Knowledge Distillation
Beyond Benchmarks - Evaluating LLMs
No More Manual Tweaking - Automated Prompt Engineering
Train Your Model - Fine-Tuning LLM
Build a Chatbot with Memory
LLMOps - Deployment and Monitoring of LLMs

M04/5 lessons

MLOps and Production Systems

Package, validate, deploy, and monitor ML services that stay up under real traffic

Understanding Your Data - Data Exploration
Fueling Production AI - Data Validation & Pipelines
Reproducible Training - ML Pipelines & Experiment Tracking
From Model to Service - Building and Dockerizing APIs
Serving at Scale - Cloud Deployment with AWS

M05/6 lessons

AI/ML Foundations

Fill in the Python, Docker, ML math, and PyTorch fundamentals you actually need.

Python for AI EngineersFree
Docker Basics for AI Apps
SQL Essentials for Keeping State
The Math That Makes AI Work
Start Simple - The Power of Linear Models
PyTorch from the Ground Up