All hyperscale AI companies build their machine learning platforms around a Feature Store. A feature is a measurable property of some data-sample. It could be for example an image-pixel, a word from a piece of text, the age of a person, a coordinate emitted from a sensor, or an aggregate value like the average number of purchases within the last hour. A Feature Store is a central place to store curated features within an organization. Feature Stores are a fuel for AI systems as we use them to train machine learning models so that we can make predictions for feature values that we have never seen before. During this presentation you learn: - About the concept of a Feature Store and how it can help manage feature data for Enterprises and ease the path of data from backend systems and data-lakes to Data Scientists. - Our take on Feature Stores, including best practices and use cases and: - How to ensure Consistent Features in both Training and Serving Governance, Access-Control, and Versioning - To create Training Data in the File Format of your Choice Eliminate Inconsistency between Features in Training and Inferencing Watch the webinar with a demo: https://www.logicalclocks.com/webinars