Exam AI-900: Microsoft Azure AI Fundamentals Crash Course
Published by Pearson
Artificial Intelligence (AI) is the capability of a computer to imitate human behavior. Microsoft Azure includes a suite of tools that enable 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.
Specifically, this Crash Course covers the objectives for Microsoft Exam AI-900: Microsoft Azure AI Fundamentals. This exam fulfills the requirements of the Microsoft Certified AI Fundamentals certification.
Note that Microsoft considers their 900-series exams to be beginner-to-intermediate level skill sets. The certification is intended for both technical and non-technical audiences, and covers the Azure AI skills from a mostly theoretical viewpoint.
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-900: Microsoft Azure AI Fundamentals
- Describe artificial intelligence workloads and considerations
- Describe fundamental machine learning concepts on Azure
- Describe computer vision workloads on Azure
- Describe conversational workloads on Azure
This live event is for you because...
- Azure AI certification candidates
- Future, present, or future data scientists or AI/ML engineers
- Azure professionals who will support Azure AI/ML projects
- Non-technical professionals who need to understand a bit of how Azure AI works
Prerequisites
The AI-900 is supposed to be a beginner/fundamentals skill set. Thus, the core prerequisite is that the learner has intermediate knowledge of Azure from any job role: administrator, developer, architect, business analyst, etc.
Ideally the learner is already a data scientist with Python, Scala, or R programming knowledge, but I'll teach to any level.
Course Set-up
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)
If the learner wants to dive in and actually practice the skills, he or she should also have:
- 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/ai900
Recommended Preparation
- Book: Pragmatic AI: An Introduction to Cloud-Based Machine Learning, First Edition, by Noah Gift https://www.oreilly.com/library/view/pragmatic-ai-an/9780134863924/
- Book: Machine Learning with Python for Everyone, by Mark Fenner https://www.oreilly.com/library/view/machine-learning-with/9780134845708/
- Book: Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications, First Edition, by Andrew Kelleher and Adam Kelleher https://www.oreilly.com/library/view/machine-learning-in/9780134116556/
Recommended Follow-up
- Book: Exam Ref AZ-900: Microsoft Azure Fundamentals, First Edition, by Jim Cheshire https://www.oreilly.com/library/view/exam-ref-az-900/9780135732199/
- Book: Microservices with Docker on Microsoft Azure (includes Content Update Program), by Boris Scholl, Trent Swanson, and Daniel Fernandez https://www.oreilly.com/library/view/microservices-with-docker/9780134218229/
- Video: Linux on Azure and LFCS Certification, by Sander van Vugt https://www.oreilly.com/learning-paths/learning-path-linux/9780135155417/
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
DAY 1 of 2 (3 hours)
Introduction (10 minutes)
Module 1: Describe AI Workloads and Considerations (75 minutes)
- Identify features of common AI workloads
- Identify guiding principles for responsible AI
Break (10 minutes)
Module 2: Describe Fundamental Principles of Machine Learning on Azure (75 minutes)
- Identify common machine learning types
- Describe core machine learning concepts
- Describe capabilities of visual tools in Azure Machine Learning Studio
Conclusion (10 minutes)
DAY 2 of 2 (3 hours)
Introduction (10 minutes)
Module 3: Describe Features of Computer Vision Workloads on Azure (75 minutes)
- Identify common types of computer vision solution
- Identify Azure tools and services for computer vision tasks
Break (10 minutes)
Module 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure (75 minutes)
- Identify features of common NLP Workload Scenarios
- Identify Azure tools and services for NLP workloads
- Identify considerations for conversational AI solutions on Azure
Conclusion (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.