While it would be great to be able to automate all aspects of machine learning operations, this currently isn’t possible. What can be reliably automated includes:
Hyperparameter optimization—uses algorithms like grid search, random search, and Bayesian methods to test combinations of pre-defined parameters and find the optimal combination.
Model selection—the same dataset is run through multiple models with default hyperparameters to determine which is best suited to learn from your data.
Feature selection—tools select the most relevant features from pre-determined sets of features.