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We are looking for a solution that will automate feature management and ensure data consistency between training and inference processing.
We expect Feature Store to enable faster and more reliable deployment of ML models in production environments by:
Eliminating redundant feature creation - computed feature definitions should be shared across models.
Ensuring consistency of computed features across exploration, training and inference - the ability to use the same computed feature definition across these stages.
Optimizing resource utilization - avoiding multiple computations of the same features compared to the current solution, where variables are calculated on the fly at each model processing stage.
Facilitating the management and monitoring of computed variables - a central repository that improves monitoring (e.g. feature stability analysis) and versioning.
As a reference - similar functionalities provided by AWS SageMaker Feature Store and Databricks Feature Store
As an example - we could use avg of SUM_DEPOSITS_EUR_OVERALL in 3 months - that definition is commonly use across different models and different stages of lifecycle of modelling - exploration, training and inference:
Transformation.avg_col(df, DEPOSIT_AVG_BAL_3M, SUM_DEPOSITS_EUR_OVERALL, windows_dict['w3'])
Needed By | Quarter |
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