In this second part of the Azure Machine Learning Series, we will discuss the following with regards to Azure Machine Learning.
Understanding Machine learning Workflows
We will see a quick recap of the introductory blog on Machine Learning. The workflows involved in Machine Learning are as follows
Challenges in MLOps
- Logically create, maintain the resources
- Keep track of ML Experiments and each Runs of experiments - Create, Reuse, delete environment with dependencies
- Opting, provisioning, re-using Local/Cloud based compute
- Maintaining different versions of Model
- Re-using the existing ML workflows
- Deploying and Maintaining the ML models
Tools to perform Machine Learning in Azure
- Azure Portal
- Azure Machine Learning Studio (UI + Coding)
- Azure Machine Learning Designer (Completely UI + less coding)
- Azure CLI
- Azure Python SDK
- Using Azure ML Studio
- Visual Studio Code + AzureML Extensions
- Other IDEs
Architecture of Azure Machine Learning
Before we get started...
- Basic Python programming Language
- Understanding of Machine Learning Workflows
- Azure Account with Subscription
- Create a Machine Learning Resource.
- Provide a name for the workspace, Container Register
- Launch the Machine Learning Studio
- Create Compute Resource
- On the left side, under Manage, select Compute.
- Select +New to create a new compute instance.
- Keep all the defaults on the first page, select Next.
- Supply a name and select Create.
- In about two minutes, you'll see the State of the compute instance change from Creating to Running. It's now ready to go.
- Create Dataset
You can create datasets from datastores, public URLs, and Azure Open Datasets.
- Launch a Notebook instance
You can follow this doc to create necessary Azure ML Resources
Creating Azure ML Resources
An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models.
You can create/access workspace by
- Using Constructor
- Using config.json file
All ML Experiments requires Compute to execute. To Create / Access the Compute Resource use ComputeTarget Class
In Azure Machine Learning, an experiment is a named process, usually the running of a script or a pipeline, that can generate metrics and outputs and be tracked in the Azure Machine Learning workspace.
An experiment can be run multiple times, with different data, code, or settings; and Azure Machine Learning tracks each run, enabling you to view run history and compare results for each run.
The Experiment Run Context
When you submit an experiment, you use its run context to initialize and end the experiment run that is tracked in Azure Machine Learning
You can log, monitor every run in the experiment.
Any Machine Learning problems involves working with Data.
It involves importing the data from the data source
Registering, Maintaining the dataset in Data Store
Versioning the dataset. You can learn more about Datasets here
Now we have all the necessary resources to train, deploy and monitor ML Models.
Stay tuned for the next blog in this series for the same.
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