MLOps, Production & Product Building Notes
Subject Overview
The final taught semester takes models out of notebooks and into real production systems that users depend on — backend engineering for AI, deployment and infrastructure, MLOps and LLM ops, and the product thinking that separates a demo from something people pay for. It closes on responsible AI, regulation, and where the frontier is heading.
Unit-wise Syllabus
8 units — click WhatsApp below to get the full notes for each
Unit 1: Backend for AI (3 hours)
FastAPI, async Python, request handling, streaming responses, authentication, rate limiting
Unit 2: Deployment & Infrastructure (4 hours)
Docker, container registries, deploying to Modal, Replicate, AWS, GCP and Vercel, GPU serving
Unit 3: MLOps Foundations (3 hours)
CI/CD for ML, model versioning, monitoring drift, A/B testing, feedback loops, cost management
Unit 4: LLM Ops & Evaluation (3 hours)
Prompt versioning, eval pipelines, observability, guardrails, caching, prompt injection defenses
Unit 5: AI Product Thinking (4 hours)
Identifying use cases where AI wins, UX patterns for AI products (streaming, citations, undo), pricing and unit economics, the 'demo versus product' gap
Unit 6: Ethics, Safety & The Frontier (3 hours)
Responsible AI, regulation (EU AI Act and others), keeping up with research, what's next — reasoning models, agents, embodied AI
Unit 7: Tools introduced
FastAPI, Docker, GitHub Actions; Modal, Replicate and Hugging Face Inference Endpoints for model serving; Vercel, Railway, Fly.io for app hosting; Posthog, Sentry, Langfuse for product and LLM observability; Stripe for monetization basics
Unit 8: Mini-projects
Deploy a fine-tuned model behind a FastAPI service with auth and logging, build a complete CI/CD pipeline for an LLM app with automated evals on each PR, launch a polished AI product on Product Hunt with payment, monitoring and feedback loops