Working with Compute

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.

If you have any feedback related to this course, please contact us at support@cloudacademy.com.

Learning Objectives

• 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

Intended Audience

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.

Prerequisites

• 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

Resources

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