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Contents
- 1 What is Azure Machine Learning?
- 2 What is Azure Machine Learning studio?
- 3 What are the different Machine learning tools provided by Microsoft Azure to fit each task?
- 4 What is the difference between the Azure ML and Azure ML Studio (classic)?
- 5 What is an Azure Machine Learning workspace?
- 6 What does the architecture of Azure Machine Learning looks like?
- 7 Final Thoughts
What is Azure Machine Learning?
Azure Machine Learning is a service for machine learning workload. It can offer from classical machine learning to deep learning, supervised, and unsupervised learning. It provides the functionality to write the ml code in Python/R. You can also do no code or minimal code based development using the azure machine learning studio. You can build, train, and track ML and deep-learning models in an Azure Machine Learning Workspace.
What is Azure Machine Learning studio?
It is a web portal provided by Microsoft Azure for machine learning capability. Using this portal you can run machine learning workload using low-code and no-code options for project authoring and asset management.
What are the different Machine learning tools provided by Microsoft Azure to fit each task?
- Azure Machine Learning designer: It provides drag-n-drop modules to build your experiments and then deploy pipelines in a low-code environment.
- Jupyter notebooks: You can create notebooks to leverage Azure SDK for Python for your machine learning.
- Machine learning extension for Visual Studio Code (preview): Full-featured development environment for building and managing your machine learning projects.
- Machine learning CLI: Azure CLI extension that provides commands for managing with Azure Machine Learning resources from the command line.
- Integration with open-source frameworks such as PyTorch, TensorFlow, and scikit-learn and many more for training, deploying, and managing the end-to-end machine learning process.
- Reinforcement learning with Ray RLlib.
What is the difference between the Azure ML and Azure ML Studio (classic)?
Difference are as follows :
| Feature | ML Studio (classic) | Azure Machine Learning |
| Drag and drop interface | It has an old portal and you get old experience. | Enhanced user experience. Having updated one portal. |
| Code SDKs | Not supported | Fully integrated with Azure Machine Learning Python and R SDKs |
| ML Pipeline | Not supported | You can build flexible, modular pipelines to automate workflows. |
| Data labeling projects | Not supported | Supported |
| Automated model training and hyperparameter tuning | Not supported | Supported |
| Experiment | Scalable (10-GB training data limit) | Scale with compute target |
| Deployment compute targets | Proprietary web service format, not customizable | Wide range of customizable deployment compute targets. Includes GPU and CPU support |
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What is an Azure Machine Learning workspace?
Azure machine learning workspace is the top-level resource for Azure Machine Learning. It is the common or central location to work with all the artifacts you create when you use Azure Machine Learning. It keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model.
What does the architecture of Azure Machine Learning looks like?
Architecture could be defined as:
