Feature Store
Every feature has
a single source of truth
Chalk’s feature store powers production ML. Define features once, and Chalk computes them on demand for training, batch scoring, and inference. Features stay fresh and consistent across environments.
Explore Chalk Model Training
One feature definition everywhere
Define once, use across training, batch, and serving.
Execution first
Instead of just storing values, compute features at query time.
Point-in-time correctness
Recompute features as they would have been at inference time. No leakage, no skew.
Iterate fast with branches
Experiment quickly and safely on branches.
Low-latency serving
Serve precomputed or on-demand features in sub-5ms at scale.
Discoverability & governance
Built-in catalog, versioning, and metadata for auditability.
Chalk has become a powerful addition to our ML infrastructure at Mission Lane, unifying and streamlining feature calculations for offline, batch evaluation, and live decisioning.
One feature catalog
Chalk isn’t just storage. It’s a query execution engine for your features. At request time, Chalk computes only what’s needed, from the freshest data available.
Online Queries
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Eliminate training-serving skew
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