Understanding the underlying architecture of a feature store is critical when exploring vendors. With so many options on the market, choosing the right feature store for your machine learning use case can be daunting.

What We’ll Cover:

• A breakdown of various feature store architectures and their pros and cons.
• Example configurations for each type and their implementation costs.
• How they can be leveraged in a real-world MLOps workflow.

Toward the end of the session, there will also be time for Q&A.

What is Featureform:

Featureform is a virtual feature store that enables data scientists to define, manage, and serve their ML model’s features. It sits atop existing infrastructure, transforming it into a traditional feature store. Using Featureform, data science teams can enhance collaboration, organize experimentation, facilitate deployment, increase reliability, and ensure compliance. It allows standardized definitions of transformations, features, labels, and training sets, making them easily shareable and understandable across teams. Additionally, Featureform is designed to work with individual data scientists and large enterprise teams, providing a centralized repository for machine learning resources.

Time: 10:00-11:00 am EST

Free!

Registration