Making the most out of the Hopsworks feature store in five minutes.

What is a feature store?

A feature store is a dual-database for storing and managing features that are used in machine learning models. These features are typically derived from raw data and are used as inputs to train and make predictions with machine learning models. A feature store enables teams to create, manage, and share features in a consistent and organized manner, which can improve the efficiency and effectiveness of building and deploying machine learning models.

How does the feature store fit in MLOps?

In the context of MLOps, the feature store serves as a central repository for storing and managing features that are used in machine learning models. The feature store enables teams to create, manage, and share features in a consistent and organized manner, which can help to improve the efficiency and effectiveness of building and deploying machine learning models. The feature store fits into the MLOps workflow by providing a source of features for training and evaluation of machine learning models, storing and managing training datasets, and enabling the use of features for prediction in production.

Can I try the feature store for free?

Hopsworks can be used for free on our serverless platform. Try it now.

Who benefits from a feature store?

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.

What can organizations achieve with a feature store?

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.

Is a feature store for machine learning only useful for large organizations?

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.

What type of infrastructure does a feature store require?

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.

Does Hopsworks offer technical support?

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