Live Oct 8: Context for Reliable Agents

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.

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Trusted by teams building the next generation of AI + ML

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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.

Mike Kuhlen ML Solutions & Strategy

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One feature catalog

@features
class Transaction:
    id: int
    amount: float
    user_id: "User.id"


@features
class User:
    id: int
    fraud_score: float = feature(version=2)
    txns: DataFrame[Transaction]
    count_small_txns: int = _.txns[_.amount < 20].count()
    raw_credit_report: str = feature(max_staleness="30d")

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.

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Eliminate training-serving drift

Talk to an engineer and see how Chalk can power your production AI and ML systems.

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Explore more of Chalk‘s data platform

LLM Toolchain

LLM Toolchain

Unify prompt engineering, embeddings, vector search, and inference to ship LLMs faster.

Temporal Aggregations

Temporal Aggregations

Define aggregations once. Reuse them across batch, online, and real-time workloads.

Training Data

Training Data

Generate point-in-time correct training datasets from production feature definitions.

Agent Deployment

Agent Deployment

Generate temporally-consistent context in your cloud.