Fully automated machine learning system which generates and optimizes solutions given a dataset and a desired outcome

A method for automated model optimization, the method comprising:

• receiving data inputs comprising a desired outcome and an input dataset identifier, the desired outcome comprising at least one of a data classification, a data regression, or a data synthesis;
• retrieving an input dataset based on the input dataset identifier;
• receiving an input model based on the desired outcome;
• creating, by using a data synthesis model, a synthetic dataset based on the input dataset and a similarity metric;
• debugging the input model using the synthetic dataset to create a debugged model;
• selecting an actual dataset based on the input dataset and the desired outcome;
• optimizing the debugged model using the actual dataset;
• and
• storing the optimized model.

A system for debugging a model, the system comprising:

• one or more memory units for storing instructions;
• and
• one or more processors configured to execute the instructions to perform operations comprising:
• receiving, via an interface, a model, a dataset, and a desired outcome comprising at least one of a data classification, a data regression, or a data synthesis;
• receiving, via the interface, a command to debug the model;
• adjusting, based on the desired outcome, the model to create an adjusted model, wherein adjusting the model comprises at least one of adjusting a hyperparameter, altering a model characteristic, editing a model parameter, or editing model code;
• training the adjusted model based on the command;
• receiving model output;
• and
• performing, based on the model output, one of terminating debugging, transmitting model output to the interface, or adjusting the adjusted model.