MLOps & Engineering
The ML lifecycle, data and experiment management, Docker and model serving, CI/CD and deployment strategies, monitoring and drift, A/B testing and responsible operations.
6 animated lectures · 64 minutes
▶ Start with lecture 1What is MLOps? The Machine Learning Lifecycle
Why a good model in a notebook is only the start: the ML lifecycle, hidden technical debt, training–serving skew, MLOps maturity levels, the tool landscape and the principles that keep models working in production.
Data Pipelines, Versioning and Experiment Tracking
Treat data like code: validation, versioning with DVC, leakage-safe splits, feature stores and point-in-time correctness, experiment tracking with MLflow, reproducibility and model registries.
Packaging and Serving Models
From a model file to a reliable service: batch vs online inference, a FastAPI endpoint, Docker images, Kubernetes and autoscaling, latency percentiles and queueing, GPU batching, optimisation and edge deployment.
CI/CD for ML and Safe Deployment Strategies
Automate the path to production: testing code, data and models; CI/CD/CT pipelines with quality gates; champion–challenger evaluation; shadow, canary and blue–green deployments; and fast rollback.
Monitoring, Drift and Retraining
Why models decay and how to catch it: what to monitor, data drift vs concept drift, the population stability index, delayed labels, alerting, incident response and retraining strategies.
A/B Testing and Responsible ML in Production
Measure real impact and operate responsibly: online vs offline evaluation, A/B test design, significance and the peeking trap, sample sizes, model cards, fairness, privacy, explainability and governance.