Classification with automated model selection, tuning, and training

A method, comprising:

• receiving, from a client, a data query referencing an input data set of a database associated with a virtual warehouse;
• allocating, by a processing device, an amount of memory of the virtual warehouse to be used to train a machine learning (ML) model based on the input data set and a peak memory estimate, wherein the peak memory estimate is based on a heuristic;
• and
• training, based on the input data set and the data query, the ML model in the virtual warehouse using the amount of memory.

A non-transitory computer-readable medium having instructions stored thereon which, when executed by a processing device, cause the processing device to:

• receive, from a client, a data query referencing an input data set of a database associated with a virtual warehouse;
• allocate, by the processing device, an amount of memory of the virtual warehouse to be used to train a machine learning (ML) model based on the input data set and a peak memory estimate, wherein the peak memory estimate is based on a heuristic;
• and
• train, based on the input data set and the data query, the ML model in the virtual warehouse using the amount of memory.