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AUTOMATICALLY SCALABLE SYSTEM FOR SERVERLESS HYPERPARAMETER TUNING

Abstract:

A scalable system and method for completing a model task using a serverless architecture is disclosed. The system may include memory storing instructions and one or more processors. The method may include receiving a request to complete a model task; retrieving a first model and a first hyperparameter based on the request; provisioning computing resources to a first development instance configured to train the first model based on the first hyperparameter and the model task; training, by the first development instance, an instance of the first model to produce a trained model and terminating said training upon satisfaction of a training criterion; receiving the trained model and a first performance metric; receiving a second performance metric associated with a second model; and terminating the development instance based on a determination that the termination condition is satisfied based on at least one of the first and second performance metrics.