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

Spis treści ebooka

• 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

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