AI/ML Engineering
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AI/ML Engineering Track | 60+ Modules | 13 Phases | ~230-310 hours
Overview
Section titled “Overview”A complete curriculum for engineers building AI and ML systems in production. Covers everything from AI-native development with Claude Code, through generative AI and RAG, to deep learning, classical ML, MLOps, and AI infrastructure on Kubernetes.
This track is for engineers who need to understand AI/ML deeply enough to build, deploy, and operate it — not just call APIs.
Phases
Section titled “Phases”| # | Phase | Focus |
|---|---|---|
| 0 | Prerequisites | Environment setup, Python, dev tools |
| 1 | AI-Native Development | Claude Code, Cursor, prompt engineering, AI coding agents |
| 2 | Generative AI | LLMs, tokenization, embeddings, text generation, reasoning models |
| 3 | Vector Search & RAG | Vector spaces, vector databases, RAG patterns, long-context |
| 4 | Frameworks & Agents | LangChain, LangGraph, LlamaIndex, agentic AI, MCP |
| 5 | MLOps & LLMOps | Kubernetes for ML, experiment tracking, pipelines, deployment |
| 6 | AI Infrastructure | Cloud management, AIOps, vLLM, GPU scheduling |
| 7 | Advanced GenAI & Safety | Fine-tuning, RLHF, diffusion, alignment, red teaming, evaluation |
| 8 | Multimodal AI | Speech, vision, video, native multimodal models |
| 9 | Deep Learning Foundations | PyTorch, neural networks, CNNs, transformers, backprop |
| 10 | Classical ML | Tabular ML, time-series, AutoML, feature stores |
| A | History of AI/ML | Historical context (appendix) |
Who This Is For
Section titled “Who This Is For”- AI/ML Engineers building production ML systems
- Platform Engineers supporting ML workloads on Kubernetes
- Backend Engineers integrating LLMs and generative AI into products
- MLOps Specialists operating model pipelines at scale
- DevOps Engineers moving into AI infrastructure roles
Prerequisites
Section titled “Prerequisites”- Programming: Python proficiency required (Phase 0 covers setup)
- Kubernetes basics: helpful for MLOps phases — see CKA track if needed
- Linux fundamentals: see Linux track if needed
- Math intuition: linear algebra and statistics helpful for deep learning phases
Related Tracks
Section titled “Related Tracks”- Platform Engineering: Data & AI — production deployment of ML systems
- CKA / CKAD — Kubernetes fundamentals for the MLOps phases
- On-Premises — running ML infrastructure on bare metal
“The best AI engineers understand both the model and the infrastructure it runs on.”