Find the best classification model with Automated Machine Learning: Review

Running the final AutoML job was the core of this module. It ran, found that a VotingEnsemble was the best model for the bank marketing dataset, and I could pull up all the metrics. It does exactly what the title says. But I felt more like an operator than a student. The exercise walks you through connecting to a workspace and creating a compute cluster using the Python SDK v2. The code is all there; my job was basically to read the comments and run the cells. No credit card, no wrestling with a local environment, just a button to launch a pre-configured Azure instance.

It was almost too smooth. I didn't have to debug anything or make any real decisions about featurization or constraints. The 'Preprocess Data' section explains what AutoML can do with data, but the exercise itself doesn't make you implement any of it. It's a demonstration—a very effective one, but a demonstration nonetheless.

I was hoping to see a bit more on interpreting the results. For example, why did LightGBM perform so well among the individual models, and what are the trade-offs of the top few candidates? The module stops right after showing you how to retrieve the best one. For something you can complete in under an hour, it's a solid, practical introduction to the Azure way of doing things. It's just a tutorial for a specific tool, and a good one at that.