Microsoft provide lot of capabilities to data science and machine learning in Microsoft Azure platform. Azure Machine Learning services is one of the most-searched terms on the internet nowadays. Azure Machine Learning , a cloud-based environment you can use to train, deploy, automate, manage, and track Machine Learning models. In Azure machine Learning service It offers an interactive and visual workspace for Data Scientists, Analysts, and Developers to make ML workflow easier to build, test, and deploy a model.

Why Azure ML ?

  1. Azure ML provides sophisticated pre-trained models such as Cognitive Services.
  2. Easy to build, test, and deploy a model on cloud.
  3. Provides DevOps for Machine Learning
  4. All Open-source frameworks like TensorFlow and Sci-kit learn, etc.. are available
  5. Flexible deployment for on-premises and cloud
  6. It provides us both drag-and-drop and coding workspace with clear documentation.

What are the benefits of Azure ML notebook VM ?

Azure ML Notebook VM is a cloud-based workstation created specifically for data scientists by Microsoft. In may 2019 Microsoft announced Azure Machine Learning service’s Notebook Virtual Machine (VM), because of resolve the resolves these conflicting requirements while simplifying the overall experience for data scientists. Its provide code first experience for python developers to build and deploy model in the workspace and perform every operation supported by the Azure Machine Learning Python SDK . There main 3 features in Notebook VM

  1. Secure and easy-to-use
    • Comapred to laaS (infrastructure-as-a-service) VM notebook VM is easy to use and less parameters.
  2. Pre-configured for machine learning
    • up-to-date AML Python Environment, GPU drivers, Pytorch, Tensorflow, Scikit learn.
  3. Fully customizable

Now lets get get into the azure ML notebook VM.

Step 1 : Login to the Azure Portal

If you don’t have an Azure Subscription you can get a free account using the link this link .

https://azure.microsoft.com/en-us/free/

Step 2 : Create Azure Machine Learning Workspace

Go to the create resource and search as a machine leaning.

Then create the machine learning workspace

after creating the machine learning workspace you should launch the machine learning studio.

Step 3 : Navigate Azure ML Compute

After launching you can see the azure ML studio workspace Home.

Now click to compute to make Virtual Machine and click NEW crete a compute instance.

Step 4 : Choose the VM size and deploy

You can choose the Machine type as GPU or CPU and Virtual Machine Size

A list of VM Sizes and pricing can be found in the documentation below link

https://azure.microsoft.com/en-us/pricing/details/virtual-machines/series/?WT.mc_id=blog-medium-abornst

When you are done click create it should take about 5–10 mins to set up the new VM depending on the specified configuration.

Step 5 : Get Start Coding with Jupyter Notebook , JupyterLab and R studio

Since all notebooks are persisted in the notebooks section of the Azure ML Service. Try if you are working with code, Notebook VM will offer you a smooth experience with Azure Machine Learning Service.

Source : https://azure.microsoft.com/en-us/blog/three-things-to-know-about-azure-machine-learning-notebook-vm/