AI Engineering Lab: Roadmap

AI engineering changes weekly. This program is built to evolve. Everything below is deliberate room for future improvement; PRs are welcome (see .github/CONTRIBUTING.md).

v1.0: current (24-week core program)

• [x] 24-week curriculum with weekly READMEs, notebooks, and use cases
• [x] Knowledge base (Ng framework, LLM core, agents, platforms, Databricks)
• [x] Skill guides: Claude Code, Cursor, DeepSeek Harness, OpenCode, OpenRouter, local stacks
• [x] Agents section: fundamentals, multi-agent, MCP, OpenClaw, Hermes
• [x] Cloud platforms: Azure AI Foundry, Google Vertex AI, AWS Bedrock
• [x] Databricks zero-to-hero module (Unity Catalog → Lakeflow → Mosaic AI → production)
• [x] Excel 24-week progress tracker with visual dashboard
• [x] Branding: Zorost Intelligence, zorost.com
• [x] Rich lesson standard, every week README is a 2,000 to 3,500-word lesson (problem, deep concepts, diagrams, worked examples, pitfalls, glossary, quiz)

v1.1: planned

• [ ] Video walkthroughs for each week (YouTube + embedded links)
• [x] Weekly quizzes, a 10-question quiz.md per week with answer key
• [ ] Docker-based lab environment (devcontainer with Jupyter + Ollama)
• [ ] GitHub Actions CI that smoke-tests every notebook on CPU
• [ ] GitHub Discussions templates for questions and week-by-week help

v1.2: candidate new modules

• [ ] Week 25+: AgentOps & Observability deep dive, LangSmith, Langfuse, OpenTelemetry for agents; cost dashboards; production tracing
• [ ] Evaluation engineering module, LLM-as-judge calibration, synthetic eval data generation, red-teaming, regression suites
• [ ] Multimodal track, vision-language models, document AI, speech pipelines
• [ ] Additional cloud platforms, Snowflake Cortex, Oracle AI, IBM watsonx
• [ ] Edge & mobile AI, on-device inference (MLX, ExecuTorch, llama.cpp on mobile)
• [ ] AI security & red-teaming track, OWASP LLM Top 10, guardrails engineering
• [ ] Regulated-industry track, audit trails, lineage, compliance patterns (extension of Zorost's aviation/pharma/gov work)
• [ ] AI product & spec shaping track, Ng's "shaping the build" in depth: MVP decisions, user research for AI, refused-tradeoff playbooks

Ideas welcomed

• Translations (content is MIT-licensed; community translations welcome)
• Regional cloud editions (more regions, sovereign-cloud notes)
• A "Fast Track" 8-week variant for experienced engineers

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