Cache systems and methods for training machine learning models

A system comprising:

• a cache;
• a processor coupled to the cache, the processor configured to:
• determine an amount of the cache for storing training data in a particular form based on a characteristic of a training dataset;
• identify a request for a first instance of the training data in the particular form to include in a training batch, wherein the first instance of the training data in the particular form is not stored in the cache;
• determine a second instance of the training data in the particular form stored in the cache has not been used to train a machine learning model;
• include the second instance of the training data in the particular form in the training batch;
• and
• train the machine learning model to perform a task using the training batch.

A method comprising:

• modeling throughput of a data storage and ingestion pipeline for training machine learning models;
• selecting an amount of a cache for storing training data in a particular form based on modeling the throughput of the data storage and ingestion pipeline;
• and
• training the machine learning models to perform a task using the training data in the particular form stored in the cache by:
• identifying a request for a first instance of the training data in the particular form to include in a training batch, wherein the first instance of the training data in the particular form is not stored in the cache;
• determining a second instance of the training data in the particular form stored in the cache has not been used to train the machine learning models;
• and
• including the second instance of the training data in the particular form in the training batch.