How “Auto” Is That AutoML?

Some AutoML systems are more automatic than others.

AutoML stands for Automated Machine Learning, meaning streamlining the end-to-end process of solving problems with machine learning. Steps that need to be automated include:

  • Data Preparation (type and dependency)
  • Feature Engineering
  • Model Algorithm Selection
  • Training
  • Hyperparameter Tuning
  • Model Evaluation
  • Production Processing and Deployment

In practice, there is a spectrum of “auto-ness” starting at “barely better than coding from scratch.” Here are some clues that a particular instance of AutoML may not be so automatic:

  • Mentions Jupyter notebooks
  • Requires knowledge of Python
  • Refers you to GitHub
  • Asks for hyperparameter ranges
  • Makes you select an algorithm
  • Omits performance metrics in output

The takeaway: Don’t assume that AutoML in the product’s description means that you can use it without programming or manual inputs.