Learn/Course 17
Production · weeks 22

Production data & ML platforms

A late feature silently corrupts every downstream prediction.

Opening production brief

A measurable problem, before a model.

Users

Operators, product owners, and people affected by the ml platforms decision.

Data

Kafka, Flink/Spark, Parquet/lakehouse, watermarks with version, owner, event time, and availability contracts.

Constraints

A late feature silently corrupts every downstream prediction. Every solution must state latency, cost, capacity, and fallback limits.

Success

Offline evidence plus a deployable resolution: A traced batch/stream platform with orchestration, registry, lineage, and ownership.

CHAPTER 1

Frame and baseline

Turn the production problem into explicit data, metric, and baseline contracts.

  1. 01

    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.

    65–90 min · checkpoint · deterministic lab
  2. 02

    Lakehouse, batch, streaming, and event time

    Reliable batch and streaming systems agree on event identity and semantics while staying honest about when information became knowable.

    65–90 min · checkpoint · deterministic lab
CHAPTER 2

Build and stress

Formalize the machinery, test counterexamples, and expose system limits.

  1. 03

    Feature platforms and point-in-time correctness

    Point-in-time correctness reconstructs what the system knew, not what the database knows now.

    65–90 min · checkpoint · deterministic lab
  2. 04

    Orchestration, metadata, experiments, and registries

    Orchestration moves work; metadata and registry gates turn that work into reproducible, promotable evidence.

    65–90 min · checkpoint · deterministic lab
CHAPTER 3

Resolve and operate

Join model behavior to architecture, observability, rollout, and rollback.

  1. 05

    Paved roads, multi-tenancy, and cost boundaries

    A good platform standardizes safety and evidence while keeping workload-specific choices visible and reversible.

    65–90 min · checkpoint · deterministic lab
  2. HW

    Local mini ML platform

    Specify idempotent events, a point-in-time join, shared feature definition, quality gates, lineage, and model registration.

    Rubric · staged hints · reference resolution
Prerequisite guidance

2 concepts make this faster.

Prerequisites are guidance, never hard gates.

C03Data & featuresData contracts, point-in-time features, backfills, quality gates, and lineage.C12GPU trainingA profiled, mixed-precision, memory-budgeted training loop.
Ready to work the problem?

Data contracts, ownership, lineage, and quality

Start lesson 01