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
The GitHub repo for this course, containing the code and datasets used, can be found here: https://github.com/cloudacademy/using-the-azure-machine-learning-sdk
Our experiments and associated resources are managed within our Azure ML Workspace. We can connect to an existing workspace or create a new one using Azure ML SDK. In most cases, you should store the webspace configuration in the JSON configuration file. This makes it easier to reconnect without needing to remember details like your Azure subscription ID. You can download the JSON configuration file from the blade for your workspace in the Azure portal. But if you're using a computer instance within your workspace, the configuration file has already been downloaded in the root folder.
The code below uses the configuration file to reconnect or to connect to your workspace. And the first time you run it in a notebook session, you'll be prompted to authenticate. So to connect, we first need to import our workspace, we then invoke the, "from config" function from our workspace library to get our workspace object. Now, this is based on a JSON configuration file we talked about earlier. We can then print the name of the workspace.
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