This article continues from the last article, "How to create an Azure Machine Learning Workspace Easily."
After that, create an Azure Machine Learning workspace, then use it to manage the varied assets and resources. You would like to make machine learning solutions. At its core, Azure Machine Learning may be a platform for training and managing machine learning models.
In Azure Machine Learning Studio Dashboard view,
Login with Created Machine Learning workspace.

When you open it for the first time, it displays a welcome view and explains the menus.

Authoring
This tool is used to build, evaluate, and deploy models.

Assets
This tool is used to prepare your data, monitor your jobs, and deploy models. Manage real-time and pipeline endpoints.

Management
This tool is used to view and manage resources for your workspace.

Compute targets
After that, already opened the Microsoft Azure Machine Learning Studio,
Then, Select and Open the Manage >> Compute.

There is four variety of compute resources you can create,
- Compute Instances- Use to work with data and models.
- Compute Clusters- Scalable clusters of the virtual machines and checkup code.
- Inference Clusters - Deployment of models.
- Attached Compute - Used to Azure compute resources (links to existing)

Create the Compute Instances
It may be a fully managed cloud-based workstation. And optimize the machine learning development environment.








Viknaraj ManogararajahPosted Jul 21, 2021, 12:45 AM
Superb Article Hadshana.