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Exam AI-100: Designing and Implementing an Azure AI Solution Crash Course

Published by Pearson

Intermediate content levelIntermediate

Artificial Intelligence (AI) is the capability of a computer to imitate human behavior. Microsoft Azure includes a suite of tools that enables AI engineers to ingest data, build machine learning (ML) models, and deploy those models as REST API web services. Azure also includes a number of platform-as-a-service cognitive services that give Azure developers easy access to pre-trained ML models.

Because AI/ML factor into most customer-facing software nowadays, coupled with the Azure AI/ML tools' steep learning curve, this training presents an excellent opportunity for IT professionals to learn the technology in a clear, low-stress manner.

A second important value proposition of this course is that it covers the objectives for Microsoft Exam AI-100: Designing and Implementing an Azure AI Solution. This exam is required to fulfill the requirements of the Microsoft Certified AI Engineer Associate professional certification.

What you’ll learn and how you can apply it

At course conclusion, you will be able to:

  • Perform additional review and then pass Exam AI-100: Designing and Implementing an Azure AI Solution
  • Analyze AI solution requirements
  • Design AI solutions using Microsoft Azure products and technologies
  • Implement and monitor those Azure-based AI solutions

This live event is for you because...

  • Azure AI Engineer certification candidates
  • Future, present, or future data scientists or AI/ML engineers
  • Azure professionals who will support Azure AI/ML projects

Prerequisites

Artificial intelligence/machine learning is a highly complex subject. Therefore, I advise learners with no previous domain experience to review relevant resources before class.

That said, neither the Microsoft exam nor this course cover AI/ML in general. Instead, the skill set is how the learner uses Microsoft Azure AI/ML technologies to plan, deliver, monitor, and maintain AI/ML functionality. It's best if the learner has prior Azure experience in the administrator or developer job roles.

Course Setup

To follow along with the demonstrations and practice on his or her own, the learner should have the following environment available:

  • Windows or macOS computer
  • Web browser and Internet connection
  • Microsoft account to create an Azure trial subscription (free)
  • Microsoft Azure 30-day trial (free)
  • A paid Azure subscription if they already used their trial
  • Visual Studio 2019 (the free Community edition is fine)
  • Visual Studio Code (free)
  • Azure Command-Line Interface (CLI) (free)
  • Docker Desktop software (free)

The course files will be available at Tim's GitHub repository: https://github.com/timothywarner/ai100

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

DAY 1

Introduction (5 minutes)

  • Set expectations
  • Perform high-level course overview
  • Provide strategies to maximize benefit from this course

Module 1: Recommend Cognitive Services APIs (40 minutes)

  • Select a processing architecture
  • Select data processing technologies

Module 2: Map Security Requirements (40 minutes)

  • Identify processes and regulations
  • Identify auditing requirements

Module 3: Select Microsoft Azure Resources (40 minutes)

  • Identify services and tools
  • Identify integration points

Break: 5 minutes

Module 4: Design AI Pipeline Solutions (40 minutes)

  • Define an AI workflow process
  • Design a data ingress/egress strategy

Module 5: Design Cognitive Services Solutions (40 minutes)

  • Design using vision speech, and language APIs
  • Design using search and anomaly detection APIs

Module 6: Design Microsoft Bot Framework Solutions (40 minutes)

  • Integrate bots and AI solutions
  • Design LUIS-capable bots

Review, wrap-up: 10 minutes

DAY 2

Introduction (5 minutes)

  • Briefly review Day 1 material
  • Perform Day 2 content overview
  • Answer any outstanding questions from Day 1

Module 7: Design a Compute Infrastructure (40 minutes)

  • Decide between GPU, FPGA, or CPU-based solutions
  • Decide between cloud-only, on-premises, or hybrid cloud infrastructures

Module 8: Design for Data Governance and Compliance (40 minutes)

  • Define authentication requirements
  • Design a content moderation strategy

Module 9: Implement an AI Workflow (40 minutes)

  • Develop AI pipelines
  • Manage data flow

Break: 5 minutes

Module 10: Integrate AI Services (40 minutes)

  • Configure Cognitive Services integration
  • Implement Azure Search

Module 11: Monitor the Environment (40 minutes)

  • Differentiate KPIs, metrics, and root causes of differences
  • Maintain AI solution for continuous improvement

Module 12: Exam AI-100 Strategy (40 minutes)

  • Microsoft Online testing process
  • Exam item tips and tricks

Review, wrap-up: 10 minutes

Your Instructor

  • Tim Warner

    Tim Warner has been a Microsoft MVP in Azure AI and Cloud/Datacenter Management for 6 years and a Microsoft Certified Trainer for more than 25 years. His O'Reilly Live Training classes on generative AI, GitHub, DevOps, data engineering, cloud computing, and Microsoft certification reach hundreds of thousands of students around the world. He's written for Microsoft Press, presented at Microsoft Ignite, and contributed to several Microsoft open-source projects. You can connect with Tim on LinkedIn: timw.info/li.

Skill covered

AI-100: Designing and Implementing an Azure AI Solution