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.