Azure AI Components and Services

By Dan Sasse

Upskill and pass the Azure AI-100 certification exam: Designing and Implementing an Azure AI Solution. This starts your journey to become an Azure AI engineer.

3 hours
  • 21 Lessons
  • 3 Hands-On Labs

About the course

Artificial Intelligence, or AI, and the Machine Learning that drives it, is one of the most exciting new frontiers of technology being presented in the cloud today. Microsoft’s Designing and Implementing an Azure AI Solution (AI-100) is an exam that covers the Azure AI services and products available.

The Certification, as described by Microsoft:

  • Candidates for this exam analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements.

  • Candidates translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. Candidates design and implement AI apps and agents that use Microsoft Azure Cognitive Services and Azure Bot Service. Candidates can recommend solutions that use open-source technologies.

  • Candidates understand the components that make up the Azure AI portfolio and the available data storage options.

  • Candidates implement AI solutions that use Cognitive Services, Azure bots, Azure Search, and data storage in Azure. Candidates understand when a custom API should be developed to meet specific requirements.

The second course covers the various components of an AI or ML based design as well as common interactions.

  • Chapter 1 3 Lessons Introduction 4:29

    An Important Note About A Cloud Guru and Linux Academy Courses


    Course Introduction


    About the Training Architect

  • Chapter 2 6 Lessons Design Solutions that Include One or More Pipelines 16:55

    Define an AI Application Workflow Process


    Design a Strategy for Ingest and Egress Data


    Design the Integration Point Between Multiple Workflows and Pipelines


    Design Pipelines that use AI Apps


    Design Pipelines that Call Azure Machine Learning Models


    Select an AI Solution that Meets Cost Constraints

  • Chapter 3 3 Lessons Design Solutions that Uses Cognitive Services 2:03:44

    Design Solutions that Use Vision, Speech, Language, Knowledge, Search, and Anomaly Detection APIs


    Performing OCR on an Image Using the .NET SDK

    1:00:00 Hands-On Lab

    Determining the Sentiment of Text Using the .NET SDK

    1:00:00 Hands-On Lab
  • Chapter 4 5 Lessons Design Solutions that Implement the Bot Framework 1:12:36

    Integrate Bots and AI Solutions


    Design Bot Services that use Language Understanding (LUIS)


    Design Bots that Integrate with Channels


    Integrate Bots with Azure App Services and Azure Application Insights


    Detecting the Language of Text Using the .NET SDK

    1:00:00 Hands-On Lab
  • Chapter 5 3 Lessons Design the Compute Infrastructure to Support a Solution 12:57

    Identify Whether to Create a GPU, FPGA, or CPU-Based Solution


    Identify Whether to Use a Cloud-Based, On-Premises, or Hybrid Compute Infrastructure


    Select a Compute Solution that Meets Cost Constraints

  • Chapter 6 1 Lesson Where do you go from here? 0:19

    Good work! Now: on to the next Course Segment!


What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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Psst…this one if you’ve been moved to ACG!