Course DP-100T01: Designing and Implementing a Data Science Solution on Azure

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Audience Profile

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Job role: Data Scientist

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Prerequisites

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques. 

Specifically:

  • Creating cloud resources in Microsoft Azure.

  • Using Python to explore and visualize data.

  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and ...

Course outline

Module 1: Getting Started with Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

  • Introduction to Azure Machine Learning

  • Working with Azure Machine Learning

Lab : Create an Azu...