concept:watermarks
Watermarks
Watermarks in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Data & feature foundations
Labels, snapshots, and backfills
Use labels, snapshots, and backfills to move the data & features production brief toward a defensible release.
Production data & ML platformsFeature platforms and point-in-time correctness
Use feature platforms and point-in-time correctness to move the ml platforms production brief toward a defensible release.
Production data & ML platformsOrchestration, metadata, experiments, and registries
Use orchestration, metadata, experiments, and registries to move the ml platforms production brief toward a defensible release.
Production data & ML platformsPaved roads, multi-tenancy, and cost boundaries
Use paved roads, multi-tenancy, and cost boundaries to move the ml platforms production brief toward a defensible release.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.