Modeling Machine Learning Data in ClickHouse

This tutorial demonstrates how to use ClickHouse as an offline feature store for machine learning models, focusing on data modeling techniques that can be used to accelerate pipelines and enable the fast building of features over potentially billions of rows. The tutorial covers the following steps: 1. Selecting a subset of data for feature engineering. 2. Creating feature tables using ClickHouse's materialized views. 3. Generating model data by joining and aligning features. 4. Generating test and training sets from the model data. 5. Using ClickHouse as an online store to serve precomputed features during inference time. By following these steps, users can build a scalable feature store that enables efficient feature engineering and accelerates machine learning pipelines.

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