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- 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 What are the components of Azure Machine Learning workspace?
- 8 What are the different ways to create Azure Machine Learning Workspace?
- 9 Why compute targets is important for Azure ML?
- 10 What are Azure Machine Learning datasets?
- 11 What do you mean by Environment in Azure ML?
- 12 Why should you use Azure ML pipelines?
- 13 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|
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:
What are the components of Azure Machine Learning workspace?
- Compute targets
- User roles
- Azure Application Insights
- Azure Key Vault
What are the different ways to create Azure Machine Learning Workspace?
There are several ways to create the Azure ml workspace, which are as follows:
Azure Portal: It is the web based feature to create the workspace. Easy to use and readily available option.
Azure Machine Learning SDK for Python: Using its workspace can be created by writing the script in python or using Jupyter notebook.
ARM Template: Azure Resource Manager template can be used to create the workspace in the automated way. It is one of the recommended approaches.
VS code extension: For the Visual Studio lovers, there comes the plugin, using that also we can do creation.
Why compute targets is important for Azure ML?
Compute target is basically a machine or could be a set of machines which is used to run notebooks or ML training scripts or host your service deployment. It is used to provide the computation power to the Azure ML to execute the ml tasks. It could be a local machine or virtual machine in the cloud. For a smaller load, a single machine based compute target would be ok. However for the production workload compute cluster is recommended.
What are Azure Machine Learning datasets?
Azure Machine Learning dataset is used to reference the data. It creates a reference to the data source location, along with a copy of its metadata. This helps in keeping only one copy of the data and could eventually save cost in case of huge data. It allows you to seamlessly access data during model training without worrying about connection strings or data paths. You can also share data and collaborate with other users. Another advantage of datasets is that they are lazily evaluated, which aids in workflow performance speeds. You can create datasets from datastores, public URLs, and Azure Open Datasets.
What do you mean by Environment in Azure ML?
Azure Machine Learning environments are an encapsulation of the environment where your machine learning training happens. It defines the Python packages, environment variables, and software settings which are needed for your scripts and notebook to execute successfully. They also specify run times (Python, Spark, or Docker). The environments are managed and versioned entities within your Machine Learning workspace that enable reproducible, auditable, and portable machine learning workflows across a variety of compute targets
Why should you use Azure ML pipelines?
Azure ML pipelines is one of the important features of Azure ML. There are many use cases where pipeline is the best option to choose. For example you can schedule steps to run in parallel or in sequence in a reliable and unattended manner. Data preparation and modeling can last days or weeks, and pipelines allow you to focus on other tasks while the process is running.
Create pipeline templates for specific scenarios, such as retraining and batch-scoring. Trigger publishes pipelines from external systems via simple REST calls. It is reusable.
You would also like to see these interview questions as well for your Azure Data science Interview :
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