Tomaz Kastrun continues an advent of Azure Machine Learning. Day 13 covers the topic of Automated ML:
Automated ML is a no-code automated machine learning task. It iterates over many combinations of algorithms and hyperparameters in order to find the best model for your dataset and your prediction variable(s). The final solution is a model, that can be downloaded and later reused. So Automated ML is not just giving you the best model out of a family of algorithms, but lets you use the model, generate the scripts and create the artefacts.
Important asset is the “Models” in navigation bar. This feature allows you to work with different model types -> custom, MLflow, and Triton. What you do here is, you register a model from different locations (e.g.: local file, AzureML Datastore, AzureML Job, MLflow Job, Model asset in AzureML workspace, and Model asset in AzureML Registry).
Once you open the Models asset, you will see, that you can do many things here. I have already model register from the running the notebook on day4.