What is the significance of 'feature store' in model deployment?
Practice Questions
Q1
What is the significance of 'feature store' in model deployment?
To store raw model outputs
To manage and serve features for model training and inference
To visualize feature importance
To automate model retraining
Questions & Step-by-Step Solutions
What is the significance of 'feature store' in model deployment?
Step 1: Understand what a feature is. A feature is a piece of information used by a machine learning model to make predictions.
Step 2: Learn about model deployment. This is the process of making a machine learning model available for use in real-world applications.
Step 3: Know that a feature store is a system that stores and manages features in one place.
Step 4: Realize that having a central place for features makes it easier to access them when training models and when the models are making predictions (inference).
Step 5: Understand that using a feature store helps ensure consistency, reduces duplication, and saves time for data scientists and engineers.
