Data processing involves transforming raw data into something meaningful that a machine learning model can use. Data transformation or encoding procedures are included in the process so that the machine can quickly decode and parse the data.  Data processing makes the input data more meaningful and informative. 

Feature store

The data sets and data pipelines required to productionize machine learning systems are managed with the help of feature stores. They enable teams to share, discover, and implement a carefully curated set of features for their machine learning problems.

Embeddings

Low-dimensional, learned continuous vector representations of discrete variables are known as embeddings, and they can decrease the dimensionality of categorical variables to represent categories in the transformed space accurately. By clustering inputs with comparable semantic characteristics together in the embedding space, an embedding ideally captures some of the semantics of the input.

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