MLOps Bootcamp: Deploy, Automate & Monitor ML in Production

A clean, reproducible project layout with virtual environments and pinned dependencies.

The Git workflow ML teams actually use — branches, pull requests, and what you must never commit.

Why your model breaks outside your laptop, and how Docker containers fix it for good.

Compute, storage, and deployment on demand — using only free tiers.

Why the same code can give different models — and the three things you must version.

Version large datasets and models alongside Git, without bloating your repository.

Automatically track every parameter, metric, and model — and compare runs.

Promote models safely from staging to production, with an instant rollback safety net.

Turn out-of-order notebooks into clean, ordered, repeatable pipelines.

Chain preprocessing and modeling into one leak-proof scikit-learn Pipeline.

Kill hard-coded values — manage every setting in one config and override from the CLI.

Run pipeline steps in the right order, on a schedule, that recover from failure.

Save models the right way — joblib for Python, ONNX for speed and portability.

Wrap your model in a production-grade prediction API in about fifteen lines.

Package your FastAPI service into one portable Docker image that runs anywhere.

Pick the right serving pattern for how fresh your predictions need to be.

ML has three moving parts — code, data, model — so CI/CD must test all three.

The three test layers that catch bad data, weak models, and broken APIs.

Automatically test and ship on every push with free GitHub Actions.

Retrain automatically when data drifts — and review the retrain like code with CML.

Ship new models safely — gradual rollouts with an escape hatch, never a blind swap.

Put your containerized model on the public internet, for free.

Scale, auto-heal, and orchestrate containers at scale with Kubernetes.

Define reproducible, reviewable cloud infrastructure as code.

How models decay silently — and the two kinds of drift you must watch for.

Make your service report its own health with structured logs and metrics.

Catch drift automatically by comparing live data against your reference set.

Chart model health and get alerted the moment something breaks.

The blueprint: how every piece connects into one end-to-end system.

Assemble a reproducible, fully tracked training pipeline with DVC + MLflow.

Turn the model into a tested, containerized service with automated CI/CD.

Go live, wire in monitoring, and close the loop with automated retraining.

Package the capstone into proof that gets you hired. → attach your GitHub repo as an External Resource here

Ten habits that separate hobby ML from production MLOps.

Learn the categories, pick one tool each, and future-proof your skills.

Turn your new skills into interviews with outcome-driven positioning.

· Your roadmap to mastery — and a send-off.