Category: Cloud

Tomaz Kastrun shares additional resources:

If you want to immerse in further reading and additional knowledge, here are some links. Here are just couple.

That’ll do it. With 24 posts in 24 days, Tomaz wraps up an excellent advent series. There’s a lot more to Azure ML but this was a great overview of AML if you’re new to the product.

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Tomaz Kastrun is winding down an advent of Azure ML. Day 22 covers batch scoring:

Batch endpoints are a great and simple way to run inference over large volumes of data. They simplify the process of hosting your models for batch scoring.

Click through to see how it all works.

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Tomaz Kastrun nears the end of the Azure ML advent. Day 20 covers package requirements and other niceties:

When creating notebooks, it is always a good way to have the dependencies included. Whether it is a particular version of a package, a separate script file or an installation requirement.

Selecting an environment or kernel can be an issue if it is not correctly initiated with the code. And you can also check the kernels with a simple python code:

Day 21 looks at the Azure CLI and running code from within a compute instance terminal:

Using Azure CLI can help you progress faster, make repetitve tasks automated and even use the GIT integration, for faster and better collaboration.

So we have created a YAML file on Day20 and we can use it also with Azure CLI to create an environment.

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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.

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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.

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