Feature caching in a feature store is the process of storing pre-computed features in a cache to improve the performance and speed of the scoring process.

To cache features for faster scoring in Databricks, first, retrieve the required features from the feature store table using the feature store API. Then, use a caching mechanism such as in-memory caching or distributed caching to store the features. Finally, use the cached features for scoring the model, which reduces the time taken to fetch the features from the feature store table.

Feature caching in a feature store is not necessary as it does not provide any significant benefits and can be avoided.

To cache features for faster scoring in Databricks, manually copy the features from the feature store table to a separate cache storage and use it for scoring the model.