AI Engineering Hub

AI Engineering from Scratch is a free, open-source curriculum developed by rohitg00 for learning to build and ship AI systems end to end. Its 20-phase sequence covers development tooling, mathematical and machine-learning foundations, deep learning, NLP, computer vision, generative AI, LLM applications, agent engineering, Model Context Protocol (MCP), agent skills, reinforcement learning, and related topics, with implementations in Python, TypeScript, Rust, and Julia. The repository describes 511 lessons and approximately 329 hours of material; each lesson produces a reusable artifact such as a prompt, skill, agent, or MCP server. Learners are instructed to read the lesson documentation, type and run the code from the repository root, record command evidence and outputs, and make a small change before continuing. It provides routes for complete foundations, mathematics and machine learning, production LLM applications, agent engineering, MCP, agent skills, and Claude certification preparation. The repository is distributed under the MIT license and is accompanied by a website containing the same lesson content.

Oracle AI Developer Hub is an actively maintained open-source repository of technical resources for building AI applications, agents, and systems with Oracle AI Database and OCI services. It covers working applications, Jupyter notebooks, architecture and memory guides, partner integrations, and hands-on workshops spanning vector search, RAG, multi-agent orchestration, persistent agent memory, hybrid retrieval, and database-backed AI systems. Examples include applications and tutorials using vector, text, graph, spatial, relational, and JSON capabilities, as well as agent checkpoints, long-term memory, semantic caches, and MCP integrations. The repository is maintained by Oracle and community contributors, and is licensed under the Universal Permissive License, Version 1.0.

AI Engineering Lab is a free, self-paced 24-week AI engineering course developed by Zorost Intelligence AI Lab. It takes learners from Python and machine learning through deep learning, LLM internals, prompt and context engineering, retrieval-augmented generation with vector search, quantization, LoRA and DPO fine-tuning, evaluation harnesses, coding-agent harnesses, AI agents, MCP, and cloud and Databricks platforms including Azure AI Foundry, Google Vertex AI, AWS Bedrock, and Databricks. The curriculum uses one continuous fictional freight-company case study. Each week pairs a concept with a runnable Python, SQL, or PySpark artifact; from Week 3 onward, the work includes a metric and an error note. The repository contains 43 runnable notebooks, publishes the full curriculum online, requires no signup or paid API key, and states that the first eight weeks require no GPU. It is MIT licensed.

DeepLearning.AI courses is an online catalog of AI education from DeepLearning.AI, covering prompting, retrieval-augmented generation, agents, generative models, LLM operations, search and retrieval, AI coding, and related subjects. The catalog is organized into short courses, broader courses, and professional certificates with beginner and intermediate levels, topic filters, and collaborator listings. Featured courses include AI Prompting for Everyone, Build with Andrew, and Agentic AI; the video notes that many courses are free and can be audited.

Hamel Husain’s Blog is a maintained technical blog and learning resource on applied AI engineering, machine learning, data science, and evaluation of AI systems. Its current focus is LLM evaluation, including debugging, analyzing, measuring, and using LLM-as-a-judge methods for AI products. The site also provides information about the AI Evals for Engineers and PMs course and an AI Evals Guide, alongside posts on tools and practices for building AI products.