Cloud based machine learning notebook feature engineering command recommender

Abstract

Embodiments operate a machine learning ('ML') notebook in a cloud infrastructure executing a plurality of ML notebooks. Embodiments receive a plurality of previously executed ML notebook feature engineering commands from the plurality of ML notebooks. Embodiments store the plurality of previously executed ML notebook feature engineering commands, including a relationship between the feature engineering commands. Embodiments mine the stored commands to generate feature engineering sets of feature engineering commands, the feature engineering sets comprising feature engineering commands that are frequently used together and an order of use of the feature engineering commands. Embodiments then receive a context of a current feature engineering command and data used in the context and recommend a next feature engineering command to be executed after the current feature engineering command.