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
- 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.