The first open-source Feature Store and the first with a DataFrame API. Most data sources (batch/streaming) supported. Ingest features using SQL, Spark, Python, Flink. The only feature store supporting stream processing for writes. Available as managed platform and on-premises.
A centralized repository for organizing, storing, and serving ML features on the GCP Vertex platform. Vertex AI Feature Store supports BigQuery, GCS as data sources. Separate ingestion jobs after feature engineering in BigQuery. Offline is BigQuery, Online BigTable.
Sagemaker Feature Store integrates with other AWS services like Redshift, S3 as data sources and Sagemaker serving. It has a feature registry UI in Sagemaker, and Python/SQL APIs. Online FS is Dynamo, offline parquet/S3.
A Feature Store built around Spark Dataframes. Supports Spark/SQL for feature engineering with a UI in Databricks. Online FS is AWS RDS/MYSQL/Aurora. Offline is Delta Lake.
FeatureForm is a virtual feature store platfrom - you plug in your offline and online data stores. It supports Flink, Snowflake, Airflow Kafka, and other frameworks.
A centralized and versioned feature storre built around their MLRun open-source MLOps orchestration framework for ML model management. Uses V3IO as it offline and online feature stores.
The platform allows to build real-time machine and deep learning features, upload ipython notebooks, monitor model drift, and set up CI/CD for machine learning systems.
FeatureByte is a solution that aims to simplify the process of preparing and managing data for AI models, making it easier for organizations to scale their AI efforts.
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Fennel is a fully managed realtime feature platform from an-Facebook team. Powered by Rust, it is built ground up to be easy to use. It works natively with Python/Pandas, has beautiful APIs, installs in your cloud in minutes, and has best-in-class support for data/feature quality.
Chalk is a platform for building real-time ML applications that includes a real-time feature store.