Developing Microsoft Azure AI Solutions

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Developing Microsoft Azure AI Solutions

Authors: Tim Warner, Sahil Malik, Jared Rhodes, Brian Harrison

Microsoft Azure offers a robust and powerful AI platform. This path teaches you how to implement an AI solution inside of Microsoft Azure. The end-to-end AI pipeline is addressed... Read more

What you will learn:

  • Define the stages of an AI pipeline inside of Microsoft Azure
  • Develop AI models and publish them as consumable endpoints
  • Build search solutions on Microsoft Azure
  • Develop solutions that leverage the AI Bot Framework to interact with users, and Intelligent Edge to collect data from the Internet of Things

Pre-requisites

This skill path is intended for learners with experience designing AI solutions, but who are novice with regards to developing them against Microsoft Azure. Prior knowledge of machine learning principles, the data science workflow, and Microsoft Azure is expected.

Beginner

In this section, you will learn to implement an end-to-end AI pipeline in Microsoft Azure.

Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure

by Tim Warner

Dec 10, 2019 / 2h 31m

2h 31m

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Description

At the core of being a Microsoft Azure AI engineer rests the need for effective collaboration. In this course, Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure, you will learn how to develop, deploy, and monitor repeatable, high-quality machine learning models with the Microsoft Azure Machine Learning service. First, you will understand how to create no-code machine learning pipelines using the Azure ML service visual designer. Next, you will explore how to train ML models using Python, Jupyter notebooks, and the Microsoft Azure Machine Learning workspace. Finally, you will discover how to monitor your Azure Machine Learning environments from the perspective of the data scientist and data engineer. When you are finished with this course, you will have a foundational knowledge of the Microsoft Azure Machine Learning service that will help you as you move forward in the Microsoft Azure AI engineer job role.

Table of contents
  1. Course Overview
  2. Understanding Machine Learning Workspaces
  3. Understanding Azure ML Pipelines
  4. Managing Machine Learning Workspaces
  5. Implementing AI Pipelines
  6. Managing Experiments
  7. Managing Data Flow and Logging

Intermediate

This section teaches you build and deploy AI models and search solutions in Microsoft Azure.

Implementing a Microsoft Azure Search Solution

by Sahil Malik

Sep 5, 2019 / 1h 19m

1h 19m

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Description

Search is so easy for users to use, and they almost expect it in every application. On the other hand, it can be so difficult to build; that simple text box in your application can mean so much work if you have to do it all by yourself. This course, Implementing a Microsoft Azure Search Solution, will help you solve the search dilemma using Azure Search - a cloud-hosted search-as-a-service platform that helps you build compelling search solutions with your data. This powerful capability is not only easy to use, but paired with Cognitive Skills it can help you unlock amazing insights in your unstructured data. At the end of this course, you will feel comfortable with the basic usage of Azure search, plugging in Cognitive Skills in the search pipeline, and administering Azure Search.

Table of contents
  1. Course Overview
  2. Understanding Azure Search
  3. Leveraging Cognitive Search
  4. Administering and Managing

Developing AI Models in Microsoft Azure

by Sahil Malik

May 31, 2019 / 1h 31m

1h 31m

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Description

AI is all around us, and it is no longer just the work of scientists. In this course, Developing AI Models in Microsoft Azure, you will learn the ins and outs of Azure Machine Learning Service. You'll start with the basics, and learn how to set up your development environment with a demo walking through each important step. With your development environment set up, you'll examine how to use the facilities of the Azure machine learning workspace, interact with it via VSCode and Jupyter notebooks using the Azure ML SDK, how to provision remote compute, and how to deploy a model to the various options, such as docker image, ACI, or AKS. By the end of this course, you will have the necessary skills to tackle any enterprise class custom AI problem in the Microsoft Azure ecosystem.

Table of contents
  1. Course Overview
  2. Approaching AI, ML Studio, and Machine Learning Service
  3. Setting up Your Dev Environment
  4. Training Your Model
  5. Deploying Your Model
  6. Wrapping Up

Advanced

In this section, you will fold IoT and Bot interfaces into your AI solution, using Microsoft Azure services.

Developing Microsoft Azure Intelligent Edge Solutions

by Jared Rhodes

Sep 12, 2019 / 2h 48m

2h 48m

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Description

Over time, what was once simply Internet of Things solutions has evolved into Edge solutions. In this course, Developing an Intelligent Edge in Microsoft Azure, you will learn foundational knowledge of edge computing, how it interacts with data and messaging systems, and how to utilize both with Microsoft Azure. First, you will learn the concepts of edge and internet of things computing. Next, you will discover how to process streaming data on hot, warm, and cold paths. Finally, you will explore how real-time and batch processing can be utilized in an edge solution. When you are finished with this course, you will have the skills and knowledge of edge and internet of things in Azure needed to architect your next edge solution. Software required: Microsoft Azure, .NET

Table of contents
  1. Course Overview
  2. Discussing Azure Iot Architecture
  3. Connecting to Iot Hub Data Streams
  4. Implementing Hot, Warm, and Cold Data Streams
  5. Creating Real-time and Batch Processes

Implementing a Microsoft Azure AI Bot Framework Solution

by Brian Harrison

Jul 26, 2019 / 2h 28m

2h 28m

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Description

When building your bot framework solution in Azure, you will need to understand how to connect it to many different AI and other data-related services as well as how to deploy it into a productionized environment. In this course, Implementing a Microsoft Azure AI Bot Framework Solution, you will gain the ability to develop and deploy a Bot Framework solution. First, you will learn how to connect all of the necessary services for your Bot Framework solution. Next, you will discover how to test the connectivity of those services as well as how to validate the output of the Bot Framework solutions activities. Finally, you will explore how to productionize your Bot Framework solution so that it can be deployed in Azure and you can feel secure about that deployment. When you’re finished with this course, you will have the skills and knowledge of Azure AI Bot Framework needed to develop, test, connect, and eventually deploy your own Bot Framework solution using any of the available connected services that Azure provides.

Table of contents
  1. Course Overview
  2. Setting up the Prerequisite Components and Input Datasets for Consuming Bot Framework
  3. Connecting Pipeline Components
  4. API Output
  5. Productionizing Your Bot