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 entity recognition

As we saw in the NLP scenarios section, entity recognition, also referred to as named entity recognition (NER), is one of the tasks that can be provided by NLP as part of the language area of AI.

Entity recognition identifies and classifies entities from a body of unstructured text (a corpus) that is recognized.

Entity recognition can provide the following uses:

  • Identifying people: Such as extracting names or celebrities from text such as newspaper articles/news feeds, and social media and how often they are mentioned/appear
  • Identifying countries, locations, places, and city names: Such as those mentioned in online holiday reviews, places of interest to visit, and the number of times mentioned; the most common that appear
  • Identifying brands: Such as those mentioned in social media and those that appear most often
  • Identifying companies, and organizations: Such as extracting from a contract agreement
  • Identifying...