MLflow
MLflow in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Data contracts, ownership, lineage, and quality
Use data contracts, ownership, lineage, and quality to move the ml platforms production brief toward a defensible release.
Production data & ML platformsLakehouse, batch, streaming, and event time
Use lakehouse, batch, streaming, and event time to move the ml platforms 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.