Running an Experiment
Running a Training Script
Datastores & Datasets
Deploying the model
The course is part of this learning path
Learn how to operate machine learning solutions at cloud scale using the Azure Machine Learning SDK. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion, data preparation, model training, and model deployment in Microsoft Azure.
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- Create an Azure Machine Learning workspace using the SDK
- Run experiments and train models using the SDK
- Optimize and manage models using the SDK
- Deploy and consume models using the SDK
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks, such as Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
- Fundamental knowledge of Microsoft Azure
- Experience writing Python code to work with data using libraries such as Numpy, Pandas, and Matplotlib
- Understanding of data science, including how to prepare data and train machine learning models using common machine learning libraries, such as Scikit-Learn, PyTorch, or Tensorflow
So now that we've created datasets that reference the diabetes data, we can register them to make them easily accessible to any experiment being one in the workspace. So we'll register the tabular dataset as diabetes dataset and then file dataset as diabetes file.
So to register our tabular dataset, we invoked our register on a tab dataset object. We specify the workspace, the name, the description, any tags, and then we specify whether we're creating a new version or not. For the file dataset, it's the same. We invoke register on the file dataset, we provide the webspace, the name, the description, tags, and whether we're creating a new version or not.
You can view monic datasets on the datasets page for your workspace in Azure ML studio. You can also get a list of datasets from the workspace object as we are doing below. The ability to version datasets enables you to redefine datasets without breaking existing experiments or pipelines that rely on previous definitions. By default, the latest version of a name dataset is returned but you can retrieve a specific version of a dataset by specifying the version number.
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