Category: Cloud

Tomaz Kastrun continues an advent of Azure ML. Day 16 shows off MLflow:

Yesterday we have looked into how to start the MLflow configurations and today, let’s put this to the test.

We will create a new notebook and use Heart dataset (link to dataset) to toy around. We will also import xgboost classifier to asses the accuracy of the presence of heart disease in the patient. We will be using a categorical (integer) variable with values from 0 (no presence) to 4 (strong presence) and attempt to classify based on 15+ attributes (out of more than 70 attributes).

Day 17 pivots to using the responsible AI dashboard:

Azure ML has provided users with collection of model and data exploration with the Studio user interface. But it also provides compatible solutions with Azure ML and Python package responsibleai. With the help of widgets, we will create an sample of dashboard to explore the solution with assessing the responsible decisions and actions.

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.

Day 14 concerns model registration:

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.

Tomaz Kastrun continues an advent series on Azure ML. Day 4 covers data sources:

Yesterday, we have learned the general outlook of the Studio and in this blog post, we will be focusing primarily on getting data to the workspace and reading data from other data sources.

Day 5 has you provision some compute:

With a basic understanding of data assets, let’s create compute instances. Under “Manage” in the navigation bar, select “Compute” (denoted as 1), select “Compute instances” (d. 2) and click on “+ New”.