We've recently launched Drifto.<p>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.<p>GitHub: <a href="https://github.com/drifto-ml/drifto" rel="nofollow">https://github.com/drifto-ml/drifto</a>
Website: <a href="https://www.driftoml.com" rel="nofollow">https://www.driftoml.com</a><p>We’d love to hear any feedback around how we can better integrate with user data pipelines or thoughts in general.