Drifto automates feature engineering over user events and transaction tables. Drifto aims to provide an automatic point-and-shoot experience: just point Drifto at any number of raw tables, and Drifto automatically combines the raw data into a large number of ML-ready features, aggregated for each ID (e.g. user) over a specified time window (e.g. weekly). Drifto supports workflows such as churn prediction, customer value estimation, anomaly detection, personalization, and demand sensing.
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[ 3.1 ms ] story [ 15.0 ms ] threadDrifto automates feature engineering over user events and transaction tables. Drifto aims to provide an automatic point-and-shoot experience: just point Drifto at any number of raw tables, and Drifto automatically combines the raw data into a large number of ML-ready features, aggregated for each ID (e.g. user) over a specified time window (e.g. weekly). Drifto supports workflows such as churn prediction, customer value estimation, anomaly detection, personalization, and demand sensing.
GitHub: https://github.com/drifto-ml/drifto Website: https://www.driftoml.com
We’d love to hear any feedback around how we can better integrate with user data pipelines or thoughts in general.