- Duration: 10 weeks
1. Design a machine learning solution
There are many options on Azure to train and consume machine learning models. Which service best fits your scenario can depend on a myriad of factors. Learn how to identify important requirements and when to use which service when you want to use machine learning models.
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2. Explore the Azure Machine Learning workspace
Throughout this learning path you’ll explore the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. You’ll also explore the various developer tools you can use to interact with the workspace.
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3. Work with data in Azure Machine Learning
Learn how to work with data in Azure Machine Learning. Whether you want to access data in notebooks or scripts, you can read data directly, through datastores, or data assets.
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4. Work with compute in Azure Machine Learning
Learn how to work with compute targets and environments in the Azure Machine Learning workspace.
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5. Automate machine learning model selection with Azure Machine Learning
Learn how to find the best model with automated machine learning (AutoML). Whether you’re training a classification, regression, or forecasting model, you can use AutoML to quickly explore various featurization techniques and algorithms.
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6. Use notebooks for experimentation in Azure Machine Learning
Learn how to use Azure Machine Learning notebooks for experimentation. Similar to Jupyter, the notebooks are ideal for exploring your data and developing a machine learning model.
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7. Train models with scripts in Azure Machine Learning
To prepare your machine learning workloads for production, you’ll work with scripts. Learn how to train models with scripts in Azure Machine Learning.
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8. Optimize model training with pipelines in Azure Machine Learning
Learn how to optimize and automate model training in Azure Machine Learning by using components and pipelines.
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9. Manage and review models in Azure Machine Learning
Learn how to manage and review models in Azure Machine Learning by using MLflow to store your model files and using responsible AI features to evaluate your models.
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10. Deploy and consume models with Azure Machine Learning
Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.
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