What is a feature store?
The feature store for machine learning is a feature computation and storage service that enables features to be registered, discovered, and used as a part of an ML pipeline. It stores, manages, and serves feature data consistently for training and inference purposes.
Feature engineering means creation of features. It is a critical component for any machine learning process. Better features lead to better models resulting in a better business outcome.
What is the importance of a feature store?
The quality of the machine learning model is not only based on the code but also on the features used for running the model. Around 80% of data scientists’ time goes into creating, training, and testing data. Feature stores save a lot of time and effort for data scientists by enabling them to reuse features instead of rebuilding these features again from scratch for different models.
What are the benefits of a feature store?
- Ensures smooth model deployment in production.
- Enhanced visibility into the overall end-to-end data flows to understand the insights generated by the models by tracking lineage and addressing regulatory compliance.
- Increased model accuracy through analysis of additional metadata for each feature.
- Facilitate collaboration by bridging the silo gap and enables teams to share their work and avoid duplication.