🎯 About this course
Training a model is the easy half. This course teaches the other half: packaging it reproducibly, promoting it through evidence-based gates, serving it behind a validated container endpoint, scaling it, and deploying with canaries and rollback. You will monitor service health and drift, design alerts that respect people's sleep, automate retraining with champion–challenger safeguards, and manage cost and reliability with unit economics and error budgets.
Labs are cloud-neutral pure Python and run anywhere; cloud services are shown in instructor-led demos so nobody is surprised by a bill. The applied studio has you deploy, break, monitor and roll back a model service and write a blameless post-mortem.
👤Who it is for
ML engineers, data scientists and DevOps engineers moving into ML operations.
✨What makes it different
Safety and rehearsal — every technique comes with a gate, a metric and a rollback.
By the end you will be able to
- Package models reproducibly with lineage metadata and manage versions and gated promotion in a registry.
- Serve models from validated, containerised endpoints and size and autoscale capacity using load-based maths.
- Build CI/CD for ML that tests code, data and models, and deploys progressively with automated rollback.
- Monitor service health, data drift, prediction drift and performance, and design actionable alerts with runbooks.
- Design safe automated retraining with triggers, champion–challenger evaluation and incident blocks.
- Manage cost and reliability using unit economics, SLOs, error budgets and graceful degradation.
- Deliver a production-style ML service with gates, monitoring, rehearsed rollback and a post-mortem.