Azure AI Services at Scale for Cloud, Mobile, and Edge - Helion

ebook

Autor: Simon Bisson, Mary Branscombe, Chris Hoder
ISBN: 9781098107994
stron: 230, Format: ebook
Data wydania: 2022-04-11
Księgarnia: Helion

Cena książki: 194,65 zł (poprzednio: 226,34 zł)
Oszczędzasz: 14% (-31,69 zł)

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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

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Spis treści

Azure AI Services at Scale for Cloud, Mobile, and Edge eBook -- spis treści

Preface
- Who This Book Is For
- How to Use This Book
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
  - From Mary Branscombe and Simon Bisson
  - From Chris Hoder
  - From Anand Raman
I. Understanding AI-Oriented Architecture
1. An Introduction to AI-Oriented Architecture
- What You Can Do with AI
- From Milestones to Models to Architectures
- Ready to Jump In?
II. Tools and Services to Help You Build AI-Oriented Architectures
2. Understanding AI Offerings and Capabilities
- AI Services for All Types of Users
- Microsofts AI Offerings
  - Managed AI Services and Infrastructure Options in Azure
  - Business Platforms with Extensible AI
  - AI for Big Data and Relational Data
  - Making Machine Learning More Portable
  - Cognitive Services
  - How to Determine What Tool Is Best for You
3. Train, Tune, and Deploy Models with Azure Machine Learning, ONNX, and PyTorch
- Understanding Azure Machine Learning
- Understanding Azure Machine Learning Studio
- Getting Started with Azure Machine Learning
  - Setting Up a Machine Learning Environment
  - Integration with Azure Services
- Using Visual Studio Code
- The Azure Machine Learning Python SDK for Local Development
- Azure Machine Learning and R
- Build Your First Model Using Azure Machine Learning Studio
  - Use Automated Machine Learning
  - Using Designer
  - Using Azure Machine Learning with Notebooks and Python
- Working with Azure Machine Learning Using Different Machine Learning Frameworks
- An Introduction to MLOps
  - Logging in Azure Machine Learning
  - Tuning Using Hyperparameters
- Exporting with ONNX
  - Using ONNX with WinML
  - Using ONNX in Machine Learning Container Runtimes
- Wrapping It Up
4. Using Azure Cognitive Services to Build Intelligent Applications
- Using Prebuilt AI
- The Core Azure Cognitive Services
  - Language
    - Translator
  - Azure OpenAI Service
  - Speech
    - Speech-to-text
    - Text-to-speech
    - Translation and unified speech
  - Vision
  - Decision Making
    - Content moderation
- Wrapping It Up
5. Using Azure Applied AI Services for Common Scenarios
- Azure Applied AI Services
  - Azure Video Analyzer
  - Cognitive Search
  - Azure Form Recognizer
  - Azure Bot Service
  - Immersive Reader
- Use Transfer Learning to Train Vision, Speech, and Language Models in Minutes
  - Creating a Custom Vision Model
  - Creating a Custom Speech Model
- Wrapping It Up
6. Machine Learning for Everyone: Low-Code and No-Code Experiences
- The Microsoft Power Platform
- Power BI and AI
  - AI Visualizations in Power BI
  - Using AI for Data Preparation in Power BI
  - Working with Custom Machine Learning Models in Power BI
  - Building Your Own Custom Models in Power BI
- AI Builder
  - Training a Custom Form Processing Model
    - Evaluating and improving models
    - How you pay for AI Builder
  - Using AI Builder Models
    - Using AI Builder in Power Automate
    - Using AI Builder in a Power App
  - Using Cognitive Services and Other AI Models in Power Automate
    - Custom connectors
    - Using Cognitive Services in Power Apps
    - Combining Power Apps and Power Automate
  - Logic Apps and AI
- Wrapping It Up
7. Responsible AI Development and Use
- Understanding Responsible AI
  - Responsible AI Improves Performance and Outcomes
  - Experiment and Iterate
- Tools for Delivering Responsible AI
  - Tools for Transparency
    - Model cards and transparency notes
    - Checklists and planning processes for AI projects
    - Interpretability
  - Tools for AI Fairness
  - Tools for Reliability and Understanding Error
  - Human in the Loop Oversight
- Wrapping It Up
- Further Resources
8. Best Practices for Machine Learning Projects
- Working Well with Data
  - Sharing Data
  - Data Provenance and Governance
    - Curating labels
    - Consider whats in your data
    - Compliance and audit
    - Security for machine learning
- Making Machine Learning Projects Successful
  - Preparing Your Dataset
  - Establish Performance Metrics
  - Transparency and Trust
  - Experiment, Update, and Move On
  - Collaboration, Not Silos
- Wrapping It Up
III. AI-Oriented Architectures in the Real World
9. How Microsoft Runs Cognitive Services for Millions of Users
- AI for Anyone
- Clusters and Containers
10. Seeing AI: Using Azure Machine Learning and Cognitive Services in a Mobile App at Scale
- Custom and Cloud Models
- The Seeing AI Backend
- Getting the Interface Right
11. Translating Multiple Languages at Scale for International Organizations
- Delivering Translations for an International Parliament
- Connecting to Existing Audio-Visual (AV) Systems
- Using Custom Speech Recognition for Specialized Vocabularies
- From Specialized Prototype to General Application
- Working within Constraints
12. Bringing Reinforcement Learning from the Lab to the Convenience Store
- Two APIs, Eight Weeks, 100% Uplift
Afterword
Index