Training Machine Learning Models with ClickHouse and Featureform

This blog post explores the use of ClickHouse as a feature store in conjunction with Featureform, an open-source Python library that enables collaboration on machine learning features and their transformations. The author demonstrates how to use SQL queries to perform data transformations and scaling operations before training logistic regression and decision tree models using incremental techniques. The post begins by providing a brief overview of feature stores and their role in the machine learning pipeline, emphasizing the importance of collaboration and reusability when working with large datasets. It then introduces ClickHouse as an ideal candidate for serving features due to its ability to handle complex queries over massive datasets quickly. Next, the author demonstrates how to use Featureform to define entities consisting of a set of features and a class label. These entities are used to register training sets, which can be efficiently iterated using Featureform's APIs. The post also explores how to split these training sets into separate training and validation datasets for model evaluation purposes. The author then proceeds to train logistic regression and decision tree models using incremental techniques such as Stochastic Gradient Descent (SGD) and the Hoeffding Adaptive Tree classifier from the River library. The performance of these models is evaluated using metrics like accuracy, confusion matrix, precision, recall, and F1-score. Finally, the post discusses how Featureform manages state and versioning by tracking lineage through Directed Acyclic Graphs (DAG) and employing techniques similar to tools such as dbt. It also highlights how this allows for collaboration on feature engineering tasks and reduces model iteration time. Overall, this blog post provides a comprehensive overview of using ClickHouse as a feature store with Featureform for training machine learning models. The author demonstrates the effectiveness of SQL-based data transformations and incremental model training techniques while emphasizing the importance of collaboration and reusability in the machine learning pipeline.