Veri 

[wtm_mlop_cats] Veri is a Feature Label Store. Feature Label store allows storing features as keys and labels as values. Querying values is only possible with knn using features. Veri also supports creating sub sample spaces of data by default. Features * Veri works as a cluster that can hold a Vector Space with fixed dimension […]

Ivory  

[wtm_mlop_cats] ivory defines a specification for how to store feature data and provides a set of tools for querying it. It does not provide any tooling for producing feature data in the first place. All ivory commands run as MapReduce jobs so it assumed that feature data is maintained on HDFS. Features Fact Sets: A […]

Hopsworks Feature Store  

[wtm_mlop_cats] Hopsworks and its Feature Store are an open source data-intensive AI platform used for the development and operation of machine learning models at scale. Features The first fully open-source Feature Store, based around Dataframes (Spark/Pandas), Hive (offline), and MySQL Cluster (online) databases. Supports model training/management/serving and Provenance. Official website Link Tutorial and documentation Click […]

Feast

[wtm_mlop_cats] Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training). Features * Operationalize your analytics data* […]

ByteHub 

[wtm_mlop_cats] ByteHub is a Python-based feature store designed to be as easy-to-use and familiar to data scientists as possible. Features Specifically ByteHub provides:* A familiar Pandas-like interface for accessing data and features;* A simple install, without any complex infrastructure to set up;* Compatibility with familiar data science tools, like Jupyter notebooks. Official website Link Tutorial […]

Butterfree

[wtm_mlop_cats] It is a feature store, as the name suggests, corresponds to an organized set of features for machine learning models. Features ETL: central framework to create data pipelines. Spark-based Extract, Transform and Load modules ready to use. Declarative Feature Engineering: care about what you want to compute and not how to code it. Feature […]