Using the Azure Machine Learning SDK
Running an Experiment
Running a Training Script
Datastores & Datasets
Deploying the model
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
We're gonna start by installing the latest version of Azure Machine Learning SDK, along with some optional packages. We will then import the azureml-core package and check the version of the SDK that is installed. So, we're doing a simple pip install here with the upgrade option. And then, we specify optional packages here, which are notebooks, azureml-widgets, automl, explain. We will be using these packages across the course. So, next, we import azureml-core. And then, we check the version details, and we have version 1.9 installed.
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