Connect service telemetry, ML pipeline dependencies, and accountable operations without mistaking uptime for model quality.
Explore observability from metrics collection and alert design to production handoffs and notebook-to-service reliability. Separate infrastructure signals from evidence about predictions; examine privacy-aware instrumentation, bounded labels, ownership, reproducible releases, and recovery planning. Activities use fictional services and do not claim that a monitoring stack guarantees reliable outcomes.
Map metrics, dashboards, alerts, data pipelines, model serving, and accountable owners in an ML service. Choose privacy-conscious service-health signals and explain what they cannot establish about predictive quality. Create operational handoff and release checks for dependencies, reproducibility, and rollback.
Working knowledge of deployed software or data pipelines and introductory ML concepts is helpful. Metrics, services, or notebook experience is useful but not required. Activities are paper-based; do not include personal request payloads in telemetry, and verify current tool documentation before implementation.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Intermediate · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.