The entire feature lifecycle managed in one feature store

Easily create features and build data pipelines with custom transformations across various data sources.

• Simplify feature creation to focus on the insights rather than infrastructure.
• Deploy data pipelines effortlessly, integrating multiple data sources to streamline your data workflow.
• Apply custom transformations to your data for full flexibility in your data processing needs.

Large-scale and cost-effective offline store for training data and a lightning-fast, low-latency online store for inference data access during online serving.

Serve features both for model training and inference.

• Ensure low-latency access to features for real-time predictions and seamless integration into your production workflows.
• Automatically maintain feature consistency across environments
• Fill in missing values for high-quality data accuracy for robust model performance.

Ingest data from data warehouses and multiple sources.

Process, extract and transform relevant features, and store them in a feature store aggregate values.

Efficiently process and manage batch feature sets for periodic tasks such as customer segmentation reports, analyzing historical data, or processing large datasets in a scheduled manner. Ensure that batch features are consistently updated and available for model training and inference.

Support real-time feature generation and processing with streaming data from Kafka. Continuously collect and process data as it is generated, enabling real-time analytics. Ideal for applications requiring real-time insights.

Enable data scientists and ML engineers to easily collaborate and share features across projects.