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Chronon simplifies data computation and serving for AI/ML apps. Users define data features, and Chronon handles batch and streaming computation, scalable backfills, low-latency serving, correctness, consistency, observability, and monitoring.
It allows you to utilize all of the data within your organization, from batch tables, event streams or services to power your AI/ML projects, without needing to worry about all the complex orchestration that this would usually entail.
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Key features:
Gather data from different sources like event streams, DB table snapshots, change data streams, service endpoints, and warehouse tables categorized as slowly changing dimensions, fact, or dimension tables.
Results can be produced in both online and offline situations. In online contexts, they can serve as scalable, low-latency endpoints for serving features. In offline scenarios, they can be stored as hive tables to generate training data.
Real-time or batch accuracy: Choose between Temporal or Snapshot accuracy for configuring the results. Temporal accuracy updates feature values in real-time for online contexts and produces point-in-time correct features offline. Snapshot accuracy updates features once a day at midnight.
Train models faster by using raw data to fill in training sets instead of waiting months to accumulate feature logs.
Utilize the robust Python API, which offers various data source types, freshness, and contexts as high-level abstractions. These are composed of intuitive SQL primitives such as group-by, join, and select, which are further enhanced with powerful features.
Automate feature monitoring by creating monitoring pipelines to assess the quality of training data, measure the difference between training and serving data, and track changes in features over time. Chronon is free on GitHub.
