Similar to DevOps, MLOps is a very broad topic and you have lots of choices when it comes to the tools that you use. In this challenge, our focus is to make sure you have right tools installed on your computer.
In this challenge we'll be setting up all the tools we will need to complete our challenges.
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Azure subscription. If you do not have one, you can sign up for a free trial.
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Azure Machine Learning service workspace - It is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models.
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Azure DevOps subscription. If you do not have one, you can sign up for a free account.
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Request Admin access to create Service Connections within Azure DevOps to connect with Azure ML Workspace. If the access is not granted, have admin create those service connections ahead of time. To do that, create a new project and create new service connections in Project Settings for your Azure Subscription and Azure ML service using Azure Resource Manager service principal.
Note: Azure ML Workspace must be created first before creating Service Connections.
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Python Installation, version at least >= 3.6.5. Anaconda is more preferred for Data Science tasks.
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Anaconda - https://docs.anaconda.com/anaconda/install/windows/
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Miniconda - https://docs.conda.io/en/latest/miniconda.html
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Python - https://www.python.org/downloads/
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Visual Studio Code or any Python IDE
- Python extensions
NOTE: You will need privileges to create projects on the DevOps account. Also, you need privileges to create Service Principal in the tenet. This translates to Ensure that the user has 'Owner' or 'User Access Administrator' permissions on the Subscription.
- You have an Azure ML Workspace created on your Azure subscription.
- You have an Azure DevOps account with Azure Machine Learning extension installed.
- You have python installed along with some IDE to run python code.