Toward the ML Pipeline

So far, we have processed the data by working on irregularities such as missing data, selected features by observing correlations, created new features, and finally ingested and versioned the processed data to the Machine learning workspace. There are two ways to fuel the data ingestion for ML model training in the ML pipeline. One way is from the central storage (where all your raw data is stored) and the second way is using a feature store. As knowledge is power, Let's get to know the use of the feature store before we move to the ML pipeline.

Feature Store

A feature store compliments the central storage by storing important features and make them available for training or inference. A feature store is a store where you transform raw data into useful features that ML models can use directly to train and infer to make predictions. Raw Data typically comes from various data sources, which are structured, unstructured, streaming, batch, and real-time...