Developing AI Applications on Azure

Developing AI Applications on Azure

Taught in English

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Gain insight into a topic and learn the fundamentals


(971 reviews)

Advanced level

Recommended experience

16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Define Artificial Intelligence and Machine Language

  • Describe AI tools and roles, and the Microsoft Team Data Science Process

  • Work with Azure APIs, including those for vision, language, and search

  • Create, train, test and deploy your AI model in the cloud

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There are 5 modules in this course

This module introduces Artificial Intelligence and Machine learning. Next, we talk about machine learning types and tasks. This leads into a discussion of machine learning algorithms. Finally we explore python as a popular language for machine learning solutions and share some scientific ecosystem packages which will help you implement machine learning. By the end of this unit you will be able to implement machine learning models in at least one of the available python machine learning libraries.

What's included

10 videos6 readings4 quizzes1 discussion prompt

This module introduces machine learning tools available in Microsoft Azure. It then looks at standardized approaches developed to help data analytics projects to be successful. Finally, it gives you specific guidance on Microsoft's Team Data Science Approach to include roles and tasks involved with the process. The exercise at the end of this unit points you to Microsoft's documentation to implement this process in their DevOps solution if you don't have your own.

What's included

9 videos2 readings3 quizzes1 discussion prompt

This module introduces you to Microsoft's pretrained and managed machine learning offered as REST API's in their suite of cognitive services. We specifically implement solutions using the computer vision api, the facial recognition api, and do sentiment analysis by calling the natural language service.

What's included

7 videos3 readings3 quizzes1 discussion prompt

This module introduces you to the capabilities of the Azure Machine Learning Service. We explore how to create and then reference an ML workspace. We then talk about how to train a machine learning model using the Azure ML service. We talk about the purpose and role of experiments, runs, and models. Finally, we talk about Azure resources available to train your machine learning models with. Exercises in this unit include creating a workspace, building a compute target, and executing a training run using the Azure ML service.

What's included

7 videos3 readings5 quizzes

This module covers how to connect to your workspace. Next, we discuss how the model registry works and how to register a trained model locally and from a workspace training run. In addition, we show you the steps to prepare a model for deployment including identifying dependencies, configuring a deployment target, building a container image. Finally, we deploy a trained model as a webservice and test it by sending JSON objects to the API.

What's included

8 videos1 reading4 quizzes


Instructor ratings
4.2 (176 ratings)
Ronald J. Daskevich, DCS
1 Course47,520 learners

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