Data
Data in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Data contracts, ownership, and lineage
Use data contracts, ownership, and lineage to move the data & features production brief toward a defensible release.
Data & feature foundationsPoint-in-time-correct features
Use point-in-time-correct features to move the data & features production brief toward a defensible release.
Data & feature foundationsFeature transformations that survive production
Use feature transformations that survive production to move the data & features production brief toward a defensible release.
Data & feature foundationsBatch, stream, and training-serving parity
Use batch, stream, and training-serving parity to move the data & features production brief toward a defensible release.
Data & feature foundationsLabels, snapshots, and backfills
Use labels, snapshots, and backfills to move the data & features production brief toward a defensible release.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.
Chronon: feature definitions across offline and online paths
The feature definition is the product; stores and compute engines are implementations.
DoorDash · Data & platformA gigascale feature store with Redis
Online feature serving is a latency-critical database product with ML semantics.