Feature Stores & Their Role In MLOPS

In machine learning, features are attributes used to describe each example. In the process known as feature engineering, transformations are applied to raw data to create features that are suitable for ML models to consume. A feature store is a tool for storing commonly used features, making them available for reuse.
A feature store transforms raw data into feature values by executing automated data pipelines, stores and manages the feature values, and retrieves data for training and scoring. The key benefit of a feature store is that it encapsulates the logic of feature transformations to automatically transform new data and serve up examples for training or scoring. If teams are continually repeating effort to code up feature transformations or copying and pasting feature-engineering code from project to project, a feature store could greatly simplify the overall process and management of features.
In this 30-minute recorded webinar, Thorogood Consultants Harpreet Cheema and Mani Singh discuss why feature stores have been increasingly gaining popularity in the industry along with the key benefits, challenges, and limitations of implementing feature stores.

[@videossss]

You read very well

[@vladislavstashek2063]

Hands on, one of the best videos on the topic. Thank you!

[@detective_kvs]

Can we use the offline feature store without internet or without any cloud services