Advent of 2022, Day 3 – Understanding Azure Machine Learning Studio | R-bloggers

Advent of 2022, Day 3 – Understanding Azure Machine Learning Studio | R-bloggers
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After creating the Azure Machine Learning workspace, you will be able not only to start the Studio but access all the necessary settings and information.

Outlook of Azure Machine Learning workspace

In this overview page, you can click the button “Launch studio” in the middle of the workspace or you can copy and paste the Studio web URL provided under the “Essentials” to start the Studio.

But before we launch the Studio, let’s explore some additional settings, worth mentioning.

Access control (IAM) – Here you will be able to view and check security access and access to the resources. You can also check the level of access a user, group, service principal, or managed identity has to this resource (this applies if you have the role of service administrator).

Events – will help you attach azure functions, webhooks, storage queues and many others when the event happens. This will be a great way to attach additional actions to events like model registration, model deployment, dataset drift detected and others.

Example of event for data drift for Storage queue

Networking – When you need to allow access to this resource through the internet. using a Private endpoint connection or public access.

Properties – is a list of all the resources with their IDs. This will become super useful when setting up the connection to other Azure services or other clients. Here you will find “Resource ID”, “Storage account ID”, “Key Vault ID”, “Container Registry ID” and others.

Tasks (preview) – is part of the automation process where tasks can be created and scheduled with automated actions – e.g.: scheduling an email for a monthly billing overview. This will create a logic app and you can also create your own logic apps and used them in tasks.

Default directory

Going back to the “Overview”, now click on the “Launch studio” and you will be redirected to the standalone site. You will get the start page of the studio – mine is called “AML_Blogpost2022” after the workspace we created on day 2.

The start page of Azure Machine Learning studio.

On the left top corner you will find the arrow with “Default Directory”. If you click on this, you will get the general overview (default directory) of Azure Machine Learning bound to your subscription (!).

Default directory of Azure Machine Learning for given subscription

In the default directory, you will be able to create new workspaces and open existing workspaces. You will also access the Registries, which will allow ML assets like components, environments, and models to be shared and used across multiple teams and workspaces in your organisation. When creating a new workspace for the first time, azureml will be generated automatically.

Azureml brings different components and environments across different regions as part of the shared assets. Under components, you can find different definitions for Designer (low-code machine learning solution builder) and also different preset environments and frameworks (like PyTorch, TensorFlow, sklearn, responsibleAI and others).

Now that you have an understanding of the default directory and registries, let’s go back to Studio. ?

Azure Machine Learning Studio

In Studio you will always have the navigation available (regardless if shown or hidden).

Navigation bar offers you three sections, Author, Assets and Manage. Each section offers different resources and each can be managed also by different users.

Under Author section, you will find:

Under Assets section:

And under Manage section:

Now that we have a basic understanding of the Studio, and the assets, we will need to get the data to our workspace. Tomorrow, we will explore the ways of getting the data.

Compete set of code, documents, notebooks, and all of the materials will be available at the Github repository:

Happy Advent of 2022! ?

This content was originally published here.