Model Training
Train models on point-in-time correct data
Chalk builds datasets as the world actually looked at each row's timestamp, from the same definitions that serve production. Train against them on infrastructure you already own.
Explore Chalk Model Training
Point-in-time correctness
Each row is built at its own timestamp, so no join leaks future data into a training example.
Fine-tune on production data
Fine-tune your own weights on Chalk using production feature data.
Same definitions as serving
The features used to train are the features that serve.
Training where the data is
Run model training on infrastructure you already own.
Train from a notebook
Start a run against production data without a handoff.
Reproducible datasets
Every revision is retrievable, so a run can be rebuilt on the exact rows it saw.
Chalk has transformed our ML development workflow. We can now build and iterate on models and ML features faster than ever, with a dramatically better developer experience.
The dataset is the hard part
Never waste a training run on bad data. Chalk builds the dataset from the same definitions that serve production, so what a model learns from and what it sees later are the same shape.
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Train on what actually happened
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