Book Image

Microsoft Azure AI Fundamentals AI-900 Exam Guide

By : Aaron Guilmette, Steve Miles
Book Image

Microsoft Azure AI Fundamentals AI-900 Exam Guide

By: Aaron Guilmette, Steve Miles

Overview of this book

The AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You’ll explore Microsoft’s responsible AI principles like safety and accountability. Then, you’ll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you’ll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you’ll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you’ll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources.
Table of Contents (20 chapters)
Free Chapter
1
Part 1: Identify Features of Common AI Workloads
4
Part 2: Describe the Fundamental Principles of Machine Learning on Azure
8
Part 3: Describe Features of Computer Vision Workloads on Azure
11
Part 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure
14
Part 5: Describe Features of Generative AI Workloads on Azure

Identify features and uses for speech recognition and synthesis

As we saw in the NLP scenarios section, speech recognition and synthesis are tasks that can be provided by NLP as part of the speech area of AI.

In the following sections, you will explore the AI capabilities of speech recognition and speech synthesis.

Speech recognition

Speech recognition is, simply put, STT; it uses the capabilities of AI to detect spoken input and output it as written text. It uses advances in areas such as DL techniques and the availability of large training datasets.

Speech recognition can provide the following uses:

  • Generating text output from users’ spoken input requests
  • Generating a text response to a user based on speech input
  • Generating audio file narration from a script for a video
  • Generating subtitles for an audience
  • Generating close captions for videos, live and recorded
  • Generating notes from dictation
  • Generating text transcripts of audio...