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- content: 'Which of these features does Azure Machine Learning studio designer have?'
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choices:
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- content: "Network Security controls and overview"
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isCorrect: false
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explanation: "Incorrect. Network security can be managed through the Azure portal and Workspace."
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- content: "Visual canvas with drag and drop controls."
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isCorrect: true
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explanation: "Correct. Azure Machine Learning Designer is found within the Azure Machine Learning Studio. It's suited for building ML pipelines by dragging and dropping modules to create workflows or tasks execute in a particular order."
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- content: "Model storage and versioning"
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isCorrect: false
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explanation: "Incorrect. Model storage and versioning can be accessed via the Workspace and Notebook feature."
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- content: 'Which of the following descriptions accurately describes a Pipeline?'
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choices:
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- content: "A coding environment for deploying and monitoring models"
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isCorrect: false
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explanation: "Incorrect. The coding environment can be found within a typical deployment pipeline but isn't a description of a pipeline."
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- content: "A top-level resource for managing all assets you create when you use Azure Machine Learning"
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isCorrect: false
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explanation: "Incorrect. This description describes a Workspace, which is where you manage all artifacts used to train and deploy models."
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- content: "A workflow of a complete machine learning process or task"
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isCorrect: true
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explanation: "Correct. Pipelines can encapsulate a task such as cleaning or extracting data or a complete workflow to train and deploy models."
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- content: 'Which MLOps principled features are found in Azure Machine Learning?'
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choices:
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- content: "Faster deployment of models"
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isCorrect: false
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explanation: "Incorrect. Yes, faster deployment is a MLOps principled feature in Azure Machine Learning, however, other features are also present within the service."
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- content: "Reproducible training pipelines and environments"
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isCorrect: false
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explanation: "Incorrect. Yes, reproducible training pipelines and environments are MLOps principled features in Azure Machine Learning, however, other features are also present within the service."
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- content: "All of the above"
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isCorrect: true
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explanation: "Correct. Azure Machine Learning incorporates all of these principles within its features to help create reproducible and reliable creation of models."
- content: 'Which of these features does Azure Machine Learning studio designer have?'
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choices:
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- content: "Network Security controls and overview"
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isCorrect: false
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explanation: "Incorrect. You can manage network security through the Azure portal and Workspace."
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- content: "Visual canvas with drag and drop controls."
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isCorrect: true
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explanation: "Correct. You can find Azure Machine Learning Designer within the Azure Machine Learning studio. It's suited for building ML pipelines by dragging and dropping modules to create workflows or tasks execute in a particular order."
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- content: "Model storage and versioning"
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isCorrect: false
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explanation: "Incorrect. You can access model storage and versioning via the Workspace and Notebook feature."
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- content: 'Which of the following descriptions accurately describes a Pipeline?'
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choices:
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- content: "A coding environment for deploying and monitoring models"
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isCorrect: false
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explanation: "Incorrect. You can find the coding environment within a typical deployment pipeline, but isn't a description of a pipeline."
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- content: "A top-level resource for managing all assets you create when you use Azure Machine Learning"
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isCorrect: false
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explanation: "Incorrect. This description describes a Workspace, which is where you manage all artifacts used to train and deploy models."
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- content: "A workflow of a complete machine learning process or task"
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isCorrect: true
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explanation: "Correct. Pipelines can encapsulate a task such as cleaning or extracting data or a complete workflow to train and deploy models."
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- content: 'Which MLOps principled features are found in Azure Machine Learning?'
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choices:
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- content: "Faster model deployment"
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isCorrect: false
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explanation: "Incorrect. Yes, faster deployment is a MLOps principled feature in Azure Machine Learning; however, other features are also present within the service."
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- content: "Reproducible training pipelines and environments"
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isCorrect: false
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explanation: "Incorrect. Yes, reproducible training pipelines and environments are MLOps principled features in Azure Machine Learning; however, other features are also present within the service."
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- content: "All of the above"
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isCorrect: true
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explanation: "Correct. Azure Machine Learning incorporates all of these principles within its features to help create reproducible and reliable creation of models."
Azure Machine Learning is a cloud-based environment where you can build and manage machine learning models. It’s designed to govern the entire ML life cycle, so you can train and deploy models without focusing on setup. The platform is suitable for any kind of machine learning, from classical to deep learning, to supervised and unsupervised learning.
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Azure Machine Learning is structured to help teams of data scientists and ML engineers make the most of their existing dataprocessing and modeldevelopment skills. Whether you use _Python_ or _R_—or have previous experience with other open-source platforms such as _PyTorch_ and _TensorFlow_—Azure Machine Learning is flexible enough to support these platforms and accelerate your work.
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Azure Machine Learning is structured to help teams of data scientists and ML engineers make the most of their existing data-processing and model-development skills. Whether you use Python or R—or have previous experience with other open-source platforms such as PyTorch and TensorFlow—Azure Machine Learning is flexible enough to support these platforms and accelerate your work.
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With in-built services, like Azure Machine Learning studio that provides a user-friendly interface, and Automated Machine Learning capabilities that assist you in model selection and training—Azure Machine Learning has tools and features to suit every level of experience.
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With in-built services like Azure Machine Learning studio that provides a user-friendly interface, and Automated Machine Learning capabilities that assist you in model selection and training, Azure Machine Learning has tools and features to suit every level of experience.
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## Prerequisites
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@@ -12,6 +12,6 @@ With in-built services, like Azure Machine Learning studio that provides a user-
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In this module, you will:
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* Assess the benefits of Azure Machine Learning
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* Describe what Azure Machine Learning is
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* Define scenarios where Azure Machine Learning can be applied
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* Assess the benefits of Azure Machine Learning.
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* Describe what Azure Machine Learning is.
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* Define scenarios where you can apply Azure Machine Learning.
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