[TBD]Role of automation in machine learning modelling

Candidate parts for automation

Hyperparameter optimization

This is typically done through the application of search algorithms, such as random search, grid search, or Bayesian optimization.

Model selection

It is done based on model performance, complexity and maintainability, as well as what resources you have available.

Feature selection

When automating feature selection testing is scripted to use one or more of a variety of algorithmic methods. After performing your feature selection tests, the one with the lowest error rate or proxy measure is selected.

Data preprocessing

Data preprocessing involves cleaning, encoding, and verifying data before use. This type of machine learning automation typically includes the detection of column types, transformation into numerical data, and handling missing values.

Advanced preprocessing can also be performed. This includes

• automation of feature selection,

• target encoding,

• data compression,

• text content processing,

• feature generation or creation, and

• data cleaning.

Transfer learning and pre-trained models

In machine learning, transfer learning involves taking models that have already been trained on a similar data set and using it for your machine learning initiative. Generally, this model is used as a base and then further trained to match your exact needs.

Search for network architecture

The neural architecture search (NAS) method is being explored and applied to problems based on gradient descent, reinforcement learning, and evolutionary algorithms. -> Understand this

Reference

https://www.run.ai/guides/machine-learning-engineering/machine-learning-automation/