AI-Powered Business Intelligence

Spis treści książki

• Preface
  • Who Should Read This Book
  • Microsoft Power BI and Azure
  • Learning Objectives
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Creating Business Value with AI
  • How AI Is Changing the BI Landscape
  • Common AI Use Cases for BI
    • Automation and Ease of Use
      • Using natural language processing to interact with data
      • Summarizing analytical results
      • Using automation to find patterns in data
    • Better Forecasting and Predictions
    • Leveraging Unstructured Data
  • Getting an Intuition for AI and Machine Learning
  • Mapping AI Use Case Ideas to Business Impact
  • Summary
• 2. From BI to Decision Intelligence: Assessing Feasibility for AI Projects
  • Putting Data First
  • Assessing Data Readiness with the 4V Framework
    • Combining 4Vs to Assess Data Readiness
  • Choosing to Make or Buy AI Services
    • AI as a Service
    • Platform as a Service
    • Infrastructure as a Service
    • End-to-End Ownership
  • Basic Architectures of AI Systems
    • User Layer
    • Data Layer
    • Analysis Layer
  • Ethical Considerations
  • Creating a Prioritized Use Case Roadmap
    • Mix Champions and Quick Wins
    • Identify Common Data Sources
    • Build a Compelling Vision
  • Summary
• 3. Machine Learning Fundamentals
  • The Supervised Machine Learning Process
    • Step 1: Collect Historical Data
    • Step 2: Identify Features and Labels
    • Step 3: Split Your Data into Training and Test Sets
    • Step 4: Use Algorithms to Find the Best Model
    • Step 5: Evaluate the Final Model
    • Step 6: Deploy
    • Step 7: Perform Maintenance
  • Popular Machine Learning Algorithms
    • Linear Regression
    • Decision Trees
    • Ensemble Learning Methods
  • Deep Learning
    • Natural Language Processing
    • Computer Vision
    • Reinforcement Learning
  • Machine Learning Model Evaluation
    • Evaluating Regression Models
    • Evaluating Classification Models
    • Evaluating Multiclassification Models
  • Common Pitfalls of Machine Learning
    • Pitfall 1: Using Machine Learning When You Dont Need It
    • Pitfall 2: Being Too Greedy
    • Pitfall 3: Building Overly Complex Models
    • Pitfall 4: Not Stopping When You Have Enough Data
    • Pitfall 5: Falling for the Curse of Dimensionality
    • Pitfall 6: Ignoring Outliers
    • Pitfall 7: Taking Cloud Infrastructure for Granted
  • Summary
• 4. Prototyping
  • What Is a Prototype, and Why Is It Important?
  • Prototyping in Business Intelligence
  • The AI Prototyping Toolkit for This Book
  • Working with Microsoft Azure
    • Sign Up for Microsoft Azure
    • Create an Azure Machine Learning Studio Workspace
    • Create an Azure Compute Resource
    • Create Azure Blob Storage
  • Working with Microsoft Power BI
  • Summary
• 5. AI-Powered Descriptive Analytics
  • Use Case: Querying Data with Natural Language
    • Problem Statement
    • Solution Overview
    • Power BI Walk-Through
  • Use Case: Summarizing Data with Natural Language
    • Problem Statement
    • Solution Overview
    • Power BI Walk-Through
  • Summary
• 6. AI-Powered Diagnostic Analytics
  • Use Case: Automated Insights
    • Problem Statement
    • Solution Overview
    • Power BI Walk-Through
  • Summary
• 7. AI-Powered Predictive Analytics
  • Prerequisites
  • About the Dataset
  • Use Case: Automating Classification Tasks
    • Problem Statement
    • Solution Overview
    • Model Training with Microsoft Azure Walk-Through
    • What Is an AutoML Job?
    • Evaluating the AutoML Outputs
    • Model Deployment with Microsoft Azure Walk-Through
    • Getting Model Predictions with Python or R
    • Model Inference with Power BI Walk-Through
    • Building the AI-Powered Dashboard in Power BI
  • Use Case: Improving KPI Prediction
    • Problem Statement
    • Solution Overview
    • Model Training with Microsoft Azure Walk-Through
    • Model Deployment with Microsoft Azure Walk-Through
    • Getting Model Predictions with Python or R
    • Model Inference with Power BI Walk-Through
    • Building the AI-Powered Dashboard in Power BI
  • Use Case: Automating Anomaly Detection
    • Problem Statement
    • Solution Overview
    • Enabling AI Service on Microsoft Azure Walk-Through
    • Getting Model Predictions with Python or R
    • Model Inference with Power BI Walk-Through
    • Building the AI-Powered Dashboard in Power BI
  • Summary
• 8. AI-Powered Prescriptive Analytics
  • Use Case: Next Best Action Recommendation
    • Problem Statement
    • Solution Overview
    • Setting Up the AI Service
    • How Reinforcement Learning Works with the Personalizer Service
    • Setting Up Azure Notebooks
    • Simulating User Interactions
    • Running the Simulation with Python
    • Evaluate Model Performance in Azure Portal
    • Model Inference with Power BI Walk-Through
    • Building the AI-Powered Dashboard in Power BI
  • Summary
• 9. Leveraging Unstructured Data with AI
  • Use Case: Getting Insights from Text Data
    • Problem Statement
    • Solution Overview
    • Setting Up the AI Service
    • Setting Up the Data Pipeline
    • Model Inference with Power BI Walk-Through
    • Building the AI-Powered Dashboard in Power BI
  • Use Case: Parsing Documents with AI
    • Problem Statement
    • Solution Overview
    • Setting Up the AI Service
    • Setting Up the Data Pipeline
    • Model Inference with Power BI Walk-Through
    • Building the AI-Powered Dashboard in Power BI
  • Use Case: Counting Objects in Images
    • Problem Statement
    • Solution Overview
    • Setting Up the AI Service
    • Setting Up the Data Pipeline
    • Model Inference with Power BI Walk-Through
    • Building the AI-Powered Dashboard in Power BI
  • Summary
• 10. Bringing It All Together: Building an AI-Powered Customer Analytics Dashboard
  • Problem Statement
  • Solution Overview
  • Preparing the Datasets
  • Allocating a Compute Resource
  • Building the ML Workflow
    • Adding Sentiment Data to the Workflow
    • Deploying the Workflow for Inference
  • Building the AI-Powered Dashboard in Power BI
    • Anomaly Detection
    • Predictive Analytics
    • AI-Powered Descriptive Analytics
    • Unstructured Data
  • Summary
• 11. Taking the Next Steps: From Prototype to Production
  • Discovery Versus Delivery
  • Success Criteria for AI Product Delivery
    • People
    • Processes
    • Data
    • Technology
  • MLOps
  • Get Started by Delivering Complete Increments
  • Conclusion
• Index

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