MLOps for Generative AI

About this course

This course contains the use of artificial intelligence. AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.

A GenAI demo takes an afternoon. A GenAI system that thousands of people rely on, that you can debug at 2 a.m., that doesn't quietly get worse or blow its budget — that takes MLOps. And generative AI breaks most of the assumptions classic MLOps was built on: the model is non-deterministic, the 'code' is partly a prompt, quality is hard to measure, and the bill is a moving target. This course teaches the discipline that closes the gap, hands-on, in Python.

It is built for machine-learning engineers, data scientists, DevOps and platform engineers, and AI architects who need to operationalize large language model applications — not just call an API once, but version it, deploy it, monitor it, evaluate it, and keep it cheap and reliable. You will not just hear concepts; you will build: a model and prompt registry, prompts managed as versioned code, a FastAPI GenAI service, containerized and scaled, with logging, tracing, evaluation, drift detection, A/B testing, and cost controls.

We go the full lifecycle: why GenAI needs its own MLOps; versioning models, prompts and data for reproducibility; prompt engineering as production code with guardrails and structured output; deploying and scaling LLM APIs; monitoring and evaluating non-deterministic output offline and online; testing, A/B-ing and retraining; and the parts that decide whether a GenAI system survives in production — cost (FinOps), security (prompt injection, secrets), governance, and reliability with fallbacks and circuit breakers.

Every cost and scale decision comes with one global, dollar-based example and one India, rupee-based example, so the trade-offs feel real wherever you work. I have spent more than twenty years putting AI and automation into real operations, living with the monitoring gaps, the surprise bills, and the 2 a.m. p

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