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A feature store can benefit anyone who works with machine learning, including data scientists, machine learning engineers, and other professionals who build and deploy machine learning models. A feature store can make it easier for these individuals to build and deploy machine learning models by providing access to pre-computed and pre-processed features in a centralized location. 

This can save time and effort by eliminating the need to manually compute and process features for each individual model. Additionally, a feature store can benefit organizations that use machine learning by enabling collaboration and sharing of features among teams.

A feature store can help organizations to improve the efficiency and effectiveness of their machine learning efforts, and can enable them to achieve their goals more quickly and with fewer resources. Among the benefits of a feature store are improved collaboration and sharing of features, faster and more efficient machine learning model development, increased reuse and building upon existing features, and better management of machine learning complexity.

A feature store for machine learning is useful for organizations of any size as it allows them to store, manage, and organize their feature data, which is essential for training and deploying machine learning models. This can help organizations improve the quality and performance of their models, streamline the ML development process, and make it easier to collaborate and share data among different teams.
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In a large organization, a feature store can help to promote collaboration and sharing of features among teams, which can improve the consistency and standardization of features across different models. This can help to prevent duplication of effort and ensure that all teams are using the same, high-quality features for their models. However, small organizations with limited resources might also benefit from using a feature store as it helps them efficiently manage and use their data.

For large organizations, the feature store is a complement to existing data pipelines and structures that serves business analysis and intelligence. As such it demands to be within an existing infrastructure (on cloud or on premise) but in the case of Hopsworks, it can also exist independently without any cloud or infrastructure in the form of a serverless service.

Hopsworks has a tiered support system for enterprise customers. We also give support via our community forum and public slack channel.