Kafka
Kafka in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Data contracts, ownership, lineage, and quality
A data contract is a shared operational promise about meaning, time, quality, and response—not a list of column types.
Production data & ML platformsLakehouse, batch, streaming, and event time
Reliable batch and streaming systems agree on event identity and semantics while staying honest about when information became knowable.
Production data & ML platformsFeature platforms and point-in-time correctness
Point-in-time correctness reconstructs what the system knew, not what the database knows now.
Production data & ML platformsOrchestration, metadata, experiments, and registries
Orchestration moves work; metadata and registry gates turn that work into reproducible, promotable evidence.
Production data & ML platformsPaved roads, multi-tenancy, and cost boundaries
A good platform standardizes safety and evidence while keeping workload-specific choices visible and reversible.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.