Run a training script as a command job in Azure Machine Learning: Review

Watching the sandbox terminal spin up without any `pip install` wrestling was a huge plus. It just worked, SDK v2 ready to go. The core task here is turning a Jupyter notebook—which we all have lying around—into a proper `.py` script that Azure ML can run as a job. The module walks through this conversion, including the crucial part of adding arguments with `argparse`. It felt very direct, like a senior dev showing me the minimum viable steps to get my code running in the cloud.

The click for me was seeing the command job being defined. Instead of just pasting a script, you define the command, the environment, and the compute target separately. This is where it goes from 'running a script' to 'managing an experiment.' The part on parameterizing the command job—being able to pass different learning rates or file paths without touching the script itself—was the key takeaway. I can already see how this stops me from having dozens of slightly different script versions. It's a small module, but it solves a very specific, very common problem that I know I'll face when moving beyond just running notebooks on my laptop.