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

Gain the necessary knowledge about how to use Azure services to develop, train and deploy machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline.

IMPORTANT NOTICE!

This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.

Audience

This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.

Prerequisites

Before attending this course, students must have the following knowledge:

  • Azure Fundamentals
  • Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
  • Know how to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.

Course content

Module 1: Doing Data Science on Azure

Introduce the Data Science Process
Overview of Azure Data Science Options
Introduce Azure Notebooks

Module 2: Doing Data Science with Azure Machine Learning service

Introduce Azure Machine Learning (AML) service
Register and deploy ML models with AML service

Module 3: Automate Machine Learning with Azure Machine Learning service

Automate Machine Learning Model Selection
Automate Hyperparameter Tuning with HyperDrive

Module 4: Manage and Monitor Machine Learning Models with the Azure Machine Learning service

Manage and Monitor Machine Learning Models

Certification

This course is recommended as preparation for exam DP-100, which leads to the new Azure Data Scientist Associate certification.

 

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