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Practical Guide to Azure Cognitive Services

Practical Guide to Azure Cognitive Services

This is the code repository for Practical Guide to Azure Cognitive Services, published by Packt.

Leverage the power of Azure OpenAI to optimize operations, reduce costs, and deliver cutting-edge AI solutions

What is this book about?

Azure Cognitive Services and OpenAI are a set of pre-built AI solution APIs that can be leveraged from existing applications, allowing customers to take advantage of Microsoft’s award-winning Vision, Speech, Text, Decision, and GPT-4 AI (artificial intelligence) capabilities.

With this book, you’ll work with manufacturing industry-specific examples of implementations to head-start your production journey. You'll begin with an overview of the categorization of Azure Cognitive Services and the benefits of embracing AI solutions for practical business applications. Next, you'll explore the benefits of using Azure Cognitive Services to optimize efficiency and improve predictive capabilities. Later, you’ll learn to leverage Vision capabilities for quality control, Forms Recognizer to streamline supply chain nuances, language understanding for improving customer service, and Cognitive Search for a next-generation knowledge mining solutions, and more.

By the end of this book, you’ll be able to implement various Cognitive Services solutions that will help you enhance efficiency, reduce costs, and improve the customer experience at your organization. You’ll also be well-equipped to automate mundane tasks by reaping the full potential of OpenAI

This book covers the following exciting features:

  • Master cost-effective deployment of Azure Cognitive Services
  • Develop proven solutions from an architecture and development standpoint
  • Understand how Cognitive Services are deployed and customized
  • Evaluate various uses of Cognitive Services with different mediums
  • Disseminate Azure costs for Cognitive Services workloads smoothly
  • Deploy next-generation Knowledge Mining solutions with Cognitive Search
  • Explore the current and future journey of OpenAI
  • Understand the value proposition of different AI projects

If you feel this book is for you, get your copy today!

https://www.packtpub.com/

Instructions and Navigations

All of the code is organized into folders.

The code will look like the following:

#!/bin/bash
python manage.py collectstatic --noinput
python manage.py migrate
python manage.py createsuperuser --noinput

Following is what you need for this book: This book is for data scientists, technology leaders, and software engineers looking to implement Azure Cognitive Services with the help of sample use cases derived from success stories. Experience with Python as well as an overall understanding of the Azure Portal with related services such as Azure Data Lake Storage and Azure Functions will help you make the most of this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-16).

Software and Hardware List

Chapter Software required OS required
1-16 Visual Studio Code with Azure extensions Windows, macOS, or Linux
1-16 Azure data tools with Azure extensions Windows, macOS, or Linux
1-16 Python versions specific to each service Windows, macOS, or Linux

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Get to Know the Author

Chris Seferlis is a Technology Strategist at Microsoft. He has over 20 years of experience working in IT and using technology to accomplish business objectives. An MBA from UMass, a Part-Time Professor at Boston University and brings a mix of business acumen, with practical technology solutions.

Christopher Nellis is a Senior Infrastructure Engineer and is experienced in deploying large-scale infrastructure for organizations. He has a passion for automation and MLOps and enjoys working with people to solve problems and make things better.

Andy Roberts is a seasoned Data and AI Architect. Having titles in his career as developer, dba, architect, project lead, and pre-sales, always revolving around data. Acquiring, shaping, moving, protecting, and using data to predict future outcomes, processing it efficiently

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