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ML in Practice — Lightweight MLOps

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The course that turns a trained model into a working production system. Reproducibility (MLflow, DVC), feature stores, model registry, batch vs online serving, FastAPI + Docker deployment, scaling and latency, A/B testing, drift detection, retraining strategy, and incident response. Six end-to-end cases mirroring what small ML teams actually build. Builds on ML Foundations; lightweight enough for teams without dedicated platform engineering.

ML in Practice — Lightweight MLOps

Duration

4 weeks

Content

4 modules, 32 lessons

Students

2

Total Time

~7 hours

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Course Curriculum

1

From Notebook to Production: The Mental ModelFREE PREVIEW

Why ML projects fail in production, reproducibility (data + code + environment + seed), feature stores and training-serving skew, versioning of models / data / features with MLflow and DVC.

80 min8 lessons

Why ML Projects Fail in Production

Recap — The Three Production Gaps

Reproducibility — Data, Code, Environment, Seed

Recap — Building a Reproducible Pipeline

Feature Stores — Why They Matter

Recap — Training/Serving Skew and How to Prevent It

Versioning — Models, Data, Features

Recap — MLflow, DVC, and Model Registry

2

Serving and DeploymentFREE PREVIEW

Batch vs online inference, building a FastAPI prediction service, Docker / Kubernetes packaging, scaling and latency budgets, A/B testing and champion-challenger model rollouts.

105 min10 lessons

Batch vs Online Inference

Recap — Choosing Your Serving Pattern

Building a Prediction Service — The FastAPI Pattern

Recap — Anatomy of a Production ML Service

Containerization and Deployment (Docker, Kubernetes)

Recap — Packaging Models for Deployment

Scaling — Autoscaling, Latency Budgets, Caching

Recap — Latency Budgets and Optimization

A/B Testing and Champion-Challenger

Recap — Safely Rolling Out New Models

3

Monitoring, Retraining, and LifecycleFREE PREVIEW

Data drift vs concept drift, performance monitoring when labels are delayed, retraining strategies (scheduled / triggered / continual), incident response and rollback playbooks.

80 min8 lessons

Drift Detection — Data Drift vs Concept Drift

Recap — Detecting and Acting on Drift

Performance Monitoring When Labels Are Delayed

Recap — Proxies and Indirect Metrics

Retraining Strategies — Scheduled, Triggered, Continual

Recap — When and How to Retrain

Incidents and Rollbacks — When Models Go Wrong

Recap — Incident Response Playbook for ML

4

Live MLOps CasesFREE PREVIEW

Six hands-on end-to-end walkthroughs: reproducible training with MLflow, containerized FastAPI deployment, drift detection setup, debugging a model that broke in prod, designing a retraining pipeline, and running a model A/B test.

150 min6 lessons

Case 1 — Build a Reproducible Training Pipeline with MLflow

Case 2 — Deploy a Model as a Containerized FastAPI Service

Case 3 — Set Up Drift Detection on Production Predictions

Case 4 — Diagnose a Model That Worked Yesterday but Not Today

Case 5 — Design a Retraining Pipeline for a Fraud Model

Case 6 — Run an A/B Test Between Two Model Versions

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