Artificial Intelligence with Microsoft Power BI

Book description

Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented software engineers and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate. Jen Stirrup, CEO of AI and BI leadership consultancy Data Relish, and Thomas J. Weinandy, research economist at Upside, show you how to use data already available to your organization.

Springboarding from the skills that you already possess, this book adds AI to your organization's technical capability and expertise with Microsoft Power BI. By using your conceptual knowledge of BI, you'll learn how to choose the right model for your AI work and identify its value and validity.

  • Use Power BI to build a good data model for AI
  • Demystify the AI terminology that you need to know
  • Identify AI project roles, responsibilities, and teams for AI
  • Use AI models, including supervised machine learning techniques
  • Develop and train models in Azure ML for consumption in Power BI
  • Improve your business AI maturity level with Power BI
  • Use the AI feedback loop to help you get started with the next project

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Table of contents

  1. 1. Getting Started with AI in the Enterprise: Your Data
    1. Workflows in Power BI using AI
      1. How are dataflows created?
      2. Things to note before creating the workflows
      3. Streaming dataflows and automatic aggregations
    2. Getting your Data Ready First
      1. Getting data ready for Dataflows
      2. Where should the data be cleaned and prepared?
    3. Real Time data ingestion vs batch processing
      1. Real time Datasets in Power BI
      2. Importing Batch Data with Power Query in Dataflows
      3. The Dataflow Calculation Engine
      4. Dataflow Options
      5. DirectQuery in Power BI
    4. Summary
  2. 2. A Great Foundation: AI and Data Modelling
    1. What is a Data Model?
      1. Why Is Data Modeling Important?
      2. Why are data models important in Power BI?
      3. Why do we need a data model for AI?
      4. Advice for setting up a data model for AI
    2. Data Modelling Disciplines to support AI
      1. Data Vault
    3. Data Modelling Versus AI Models
      1. Data modeling in Power BI
      2. What do Relationships Mean for AI?
      3. Power BI Flat File Structure vs Dimensional Model Structure
  3. 3. Blueprint for AI in the Enterprise
    1. What Is a Data Strategy?
    2. Artificial Intelligence in Power BI Data Visualization
      1. The Power BI Decomposition Tree
      2. Power BI Key Influencer Visuals
      3. Q&A Visual
    3. Insights Using AI
      1. Using AI to Reduce Cognitive Load
    4. Automated Machine Learning (AutoML) in Power BI
      1. Cognitive Services
      2. Data Modeling
    5. Real-World Problem-Solving with Data
      1. Binary Prediction
      2. Classification
      3. Regression
    6. Practical Demonstration of Binary Prediction to Predict Income Levels
      1. Gathering the Data
      2. Create a Workspace
      3. Create a Dataflow
    7. Model Evaluation Reports in Power BI
      1. Prediction Report
      2. Accuracy Report
      3. Training Report
    8. Summary
  4. 4. Power BI with Text Analytics
    1. Text as Data
    2. Limitations of Text Analytics
    3. Demo Part 1: Ingest AirBnB Data
      1. Language Detection
        1. How It Works
        2. Performance and limitations
        3. Demo Part 2: Language Detection
      2. Key Phrase Extraction
        1. How It Works
        2. Performance and Limitations
        3. Demo Part 3: Key Phrase Extraction
      3. Sentiment analysis
        1. How It Works
        2. Recommendations and Limitations
        3. Demo Part 4: Sentiment Analysis
      4. Conclusion
        1. Demo Part 5: Exploring a Report with Text Analytics
  5. 5. Image Tagging
    1. Images as Data
    2. Deep Learning
    3. A Simple Neural Network
    4. Image Tagging for Business
    5. How It Works
    6. Limitations of Vision
    7. Demo Part 1: Ingest AirBnB Data
    8. Demo Part 2: Image Tagging
    9. Demo Part 3: Exploring a Report with Vision
    10. Conclusion
  6. 6. Custom Machine Learning Models
    1. AI Business Strategy
      1. Organizational Learning with AI
      2. Successful Organizational Behaviors
    2. Custom Machine Learning
      1. Machine Learning versus Typical Programming
      2. Narrow AI versus General AI
    3. Azure Machine Learning
      1. Azure Subscription and Free Trial
      2. Azure Machine Learning Studio
    4. Demo 9-1: Forecasting Vending Machine Sales
      1. Creating an Azure Machine Learning workspace
      2. Training a Custom Model in Azure Machine Learning Studio
      3. Deploying a Custom Model in Azure Machine Learning
      4. Consuming a Custom Model in Power BI
    5. Summary
  7. About the Authors

Product information

  • Title: Artificial Intelligence with Microsoft Power BI
  • Author(s): Jen Stirrup, Thomas J. Weinandy
  • Release date: April 2024
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098112752