Data
Data in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Data contracts, ownership, and lineage
A trustworthy dataset explains meaning, accountability, and ancestry—not just columns.
Data & feature foundationsPoint-in-time-correct features
Point-in-time correctness preserves the knowledge boundary of the original decision.
Data & feature foundationsFeature transformations that survive production
Ship the fitted representation contract with the model, in the same order and with the same edge cases.
Data & feature foundationsBatch, stream, and training-serving parity
Parity starts with one temporal meaning, then proves every execution path implements it.
Data & feature foundationsLabels, snapshots, and backfills
A label snapshot records what was knowable by a date, not the database’s latest opinion.
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.