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Azure AI Services at Scale for Cloud, Mobile, and Edge: Building Intelligent Apps with Azure Cognitive Services and Machine Learning [Pehme köide]

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  • Formaat: Paperback / softback, 230 pages
  • Ilmumisaeg: 30-Apr-2022
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1098108043
  • ISBN-13: 9781098108045
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Azure AI Services at Scale for Cloud, Mobile, and Edge: Building Intelligent Apps with Azure Cognitive Services and Machine LearningSuurem pilt
  • Formaat: Paperback / softback, 230 pages
  • Ilmumisaeg: 30-Apr-2022
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1098108043
  • ISBN-13: 9781098108045
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781098108045.html
Märksõnad:

Take advantage of the power of cloud and the latest AI techniques. Whether you're an experienced developer wanting to improve your app with AI-powered features or you want to make a business process smarter by getting AI to do some of the work, this book's got you covered. Authors Anand Raman, Chris Hoder, Simon Bisson, and Mary Branscombe show you how to build practical intelligent applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

This book shows you how cloud AI services fit in alongside familiar software development approaches, walks you through key Microsoft AI services, and provides real-world examples of AI-oriented architectures that integrate different Azure AI services. All you need to get started is a working knowledge of basic cloud concepts.

  • Become familiar with Azure AI offerings and capabilities
  • Build intelligent applications using Azure Cognitive Services
  • Train, tune, and deploy models with Azure Machine Learning, PyTorch, and the Open Neural Network Exchange (ONNX)
  • Learn to solve business problems using AI in the Power Platform
  • Use transfer learning to train vision, speech, and language models in minutes

Preface x
Part I Understanding AI-Oriented Architecture
1 An Introduction to AI-Oriented Architecture
3(8)
What You Can Do with AI
3(1)
From Milestones to Models to Architectures
4(3)
Ready to Jump In?
7(4)
Part II Tools and Services to Help You Build AI-Oriented Architectures
2 Understanding AI Offerings and Capabilities
11(16)
AI Services for All Types of Users
11(3)
Microsoft's AI Offerings
14(13)
Managed AI Services and Infrastructure Options in Azure
15(2)
Business Platforms with Extensible AI
17(1)
AI for Big Data and Relational Data
17(1)
Making Machine Learning More Portable
18(1)
Cognitive Services
19(2)
How to Determine What Tool Is Best for You
21(6)
3 Train, Tune, and Deploy Models with Azure Machine Learning, ONNX, and PyTorch
27(22)
Understanding Azure Machine Learning
27(1)
Understanding Azure Machine Learning Studio
28(1)
Getting Started with Azure Machine Learning
29(3)
Setting Up a Machine Learning Environment
29(2)
Integration with Azure Services
31(1)
Using Visual Studio Code
32(1)
The Azure Machine Learning Python SDK for Local Development
33(2)
Azure Machine Learning and R
35(2)
Build Your First Model Using Azure Machine Learning Studio
37(4)
Use Automated Machine Learning
37(1)
Using Designer
38(1)
Using Azure Machine Learning with Notebooks and Python
39(2)
Working with Azure Machine Learning Using Different Machine Learning Frameworks
41(2)
An Introduction to MLOps
43(3)
Logging in Azure Machine Learning
45(1)
Tuning Using Hyperparameters
45(1)
Exporting with ONNX
46(2)
Using ONNX with WinML
47(1)
Using ONNX in Machine Learning Container Runtimes
47(1)
Wrapping It Up
48(1)
4 Using Azure Cognitive Services to Build Intelligent Applications
49(30)
Using Prebuilt AI
49(5)
The Core Azure Cognitive Services
54(23)
Language
55(6)
Azure OpenAI Service
61(3)
Speech
64(5)
Vision
69(5)
Decision Making
74(3)
Wrapping It Up
77(2)
5 Using Azure Applied AI Services for Common Scenarios
79(24)
Azure Applied AI Services
79(15)
Azure Video Analyzer
81(2)
Cognitive Search
83(3)
Azure Form Recognizer
86(5)
Azure Bot Service
91(2)
Immersive Reader
93(1)
Use Transfer Learning to Train Vision, Speech, and Language Models in Minutes
94(7)
Creating a Custom Vision Model
95(4)
Creating a Custom Speech Model
99(2)
Wrapping It Up
101(2)
6 Machine Learning for Everyone: Low-Code and No-Code Experiences
103(36)
The Microsoft Power Platform
104(1)
Power BI and AI
105(8)
AI Visualizations in Power BI
107(2)
Using AI for Data Preparation in Power BI
109(1)
Working with Custom Machine Learning Models in Power BI
110(1)
Building Your Own Custom Models in Power BI
111(2)
AI Builder
113(25)
Training a Custom Form Processing Model
116(3)
Using AI Builder Models
119(7)
Using Cognitive Services and Other AI Models in Power Automate
126(8)
Logic Apps and AI
134(4)
Wrapping It Up
138(1)
7 Responsible AI Development and Use
139(20)
Understanding Responsible AI
141(2)
Responsible AI Improves Performance and Outcomes
142(1)
Experiment and Iterate
143(1)
Tools for Delivering Responsible AI
143(12)
Tools for Transparency
145(5)
Tools for AI Fairness
150(2)
Tools for Reliability and Understanding Error
152(1)
Human in the Loop Oversight
153(2)
Wrapping It Up
155(1)
Further Resources
156(3)
8 Best Practices for Machine Learning Projects
159(14)
Working Well with Data
159(5)
Sharing Data
160(1)
Data Provenance and Governance
160(4)
Making Machine Learning Projects Successful
164(6)
Preparing Your Dataset
165(2)
Establish Performance Metrics
167(2)
Transparency and Trust
169(1)
Experiment, Update, and Move On
169(1)
Collaboration, Not Silos
170(1)
Wrapping It Up
170(3)
Part III AI-Oriented Architectures in the Real World
9 How Microsoft Runs Cognitive Services for Millions of Users
173(6)
AI for Anyone
174(2)
Clusters and Containers
176(3)
10 Seeing AI: Using Azure Machine Learning and Cognitive Services in a Mobile App at Scale
179(6)
Custom and Cloud Models
180(2)
The Seeing AI Backend
182(1)
Getting the Interface Right
183(2)
11 Translating Multiple Languages at Scale for International Organizations
185(4)
Delivering Translations for an International Parliament
185(1)
Connecting to Existing Audio-Visual (AV) Systems
186(1)
Using Custom Speech Recognition for Specialized Vocabularies
186(1)
From Specialized Prototype to General Application
187(1)
Working within Constraints
188(1)
12 Bringing Reinforcement Learning from the Lab to the Convenience Store
189(6)
Two APIs, Eight Weeks, 100% Uplift
190(5)
Afterword 195(4)
Index 199
Simon Bisson is a freelance writer, specializing in enterprise technologies and development. He writes for several publications, including InfoWorld, ZDNet, and Computerworld.

Anand Raman leads the program management for AI Services Platform at Microsoft. Previously, he was the chief of staff for the Microsoft Azure AI and Data Group, covering data platforms and machine learning, and ran the company's product management and the development teams for Azure Data Services and the Visual Studio and Windows Server user experience teams; he also worked several years as researcher before joining Microsoft. Anand holds a PhD in computational fluid mechanics.

Mary Branscombe has worked as a freelance technology journalist for many publications over three decades. Recently she's specialized in AI, enterprise technology, and development.

Chris Hoder is a Program Manager on the Cognitive Services team at Microsoft. Chris focuses on the end-to-end developer experience across the entire suite of services - from our API and SDK designs to the getting started documentation. In prior roles, Chris worked directly with customers to envision, design, build and deploy AI-focused applications using Microsoft's AI stack.