Detection of similar machine learning features in real-time for declarative feature engineering

A method comprising:

• presenting a user interface comprising a plurality of options for a machine learning (ML) model configuration of an ML model by an ML model training system;
• receiving a feature parameter for a feature usable by the ML model for an ML model output task;
• generating a feature vector for the feature based at least on the feature parameter;
• accessing a plurality of feature scores for a plurality of features preexisting with the ML model training system, wherein the plurality of feature scores are associated with a plurality of feature vectors for the plurality of features;
• calculating a weighted distance between the feature vector and each of the plurality of feature vectors;
• determining a first one of the plurality of features having a corresponding one of the weighted distances within a threshold distance to the feature vector;
• and
• presenting the first one of the plurality of features in the user interface.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• receiving a plurality of machine learning (ML) features associated with at least one ML model deployed with decision services of a server provider system, wherein each of the plurality of ML features is associated with a measurable datum used by one or more of the at least one ML model for ML model outputs;
• converting feature description data for each of the plurality of ML features to a corresponding vector representable in a vector space;
• clustering the corresponding vectors for the plurality of ML features in the vector space using an ML clustering technique, wherein the ML clustering technique utilizes similarity scores between the corresponding vectors;
• generating a cluster representative vector for each cluster from a plurality of clusters resulting from the clustering;
• and
• storing cluster comparison data comprising the cluster representative vectors and the plurality of clusters that enables use with a feature engineering operation of an application.