Concerned about Tecton? Watch now.

Chalk for Data Scientists

Discover and create features, generate reproducible datasets, and accelerate AI and ML experiments with Chalk.

TALK TO AN ENGINEER
hero gradient image

Trusted by teams building the next generation of AI + ML

logologologologologo

Why data scientists choose Chalk

product section resource

Data Scientists move quickly from hypothesis to production. With Chalk they:

  • Get observability into the features they serve
  • Build training datasets with lineage
  • Trust that training and production always stay in sync
Learn More

By moving our feature pipelines to Chalk, Data Science and Engineering now work side-by-side throughout model development. What used to be lengthy, error-prone handoffs are gone. Our entire search ranking stack runs on Chalk, serving features for inference in under 50ms, and we’re extending it to all of our models, including real‑time personalization.

Moaj Mustang Head of Engineering

company logo

Point-in-time correctness

product section resource

Training data should reflect what your application would have known at the time. Chalk guarantees this with point-in-time lookups:

  • Avoid leakage by using only values available at the correct historical moment
  • Generate reproducible datasets with complete lineage
  • Backtest models with confidence, knowing inputs match production
LEARN MORE

Built for notebooks

@features
class Review:
    id: int
    at: FeatureTime
    product_id: "Product.id"
    review_body: str
    rating: int

@features
class Product:
    id: int
    title: str
    reviews: DataFrame[Review]
    average_rating: float | None = _.reviews[_.rating].mean()
    last_rating: int | None = _.reviews[_.rating].max_by(_.at)
    review_count: Windowed[int] = windowed(
        "30d",
        "90d",
        "all",
        expression=_.reviews[
          _.created_at > _.chalk_window
        ].count(),
    )

Chalk integrates directly with the tools data scientists use every day, enabling them to explore, iterate, and ship models without leaving their notebook.

  • Iterate on features and model training directly in your notebook
  • Deploy, backfill, and monitor datasets for training pipelines
  • Experiment and define new features independently while Chalk handles orchestration
Notebook tutorial

Explore how Chalk works

product

Chalk for Data Scientists

2025.09.10

customer story

Medely staffs critical healthcare in real-time with Chalk

2025.12.11

product

Quarterly Product Update: Fall

2025.11.10

engineering

Which LLM Wins at Nolan Trivia? Chalk’s Prompt Evaluation in Production

2025.06.02

Ready to ship next‑gen ML?

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

TALK TO AN ENGINEER