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 of OCR solutions

The Computer Vision (CV) capabilities of Azure Machine Learning can be used as a solution for OCR.

The OCR solution can be used to extract “text” from an image. Letters and numbers are identified from shapes and then converted into machine-encoded text that can then be further utilized for processing by applications or users.

An example of OCR for an image can be seen in Figure 6.7:

Figure 6.7 – Extracting text from an image with OCR capability

Figure 6.7 – Extracting text from an image with OCR capability

The OCR model is trained to recognize elements of text, including punctuation, as well as numerals from individual shapes, and then produce an output as text. An example of a text output produced by an OCR model is shown in Figure 6.8:

Figure 6.8 – OCR model text extraction output

Figure 6.8 – OCR model text extraction output

The following is an extract of the corresponding API’s JSON response for the object detection from Figure 6.8:

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