Artificial Intelligence with Microsoft Power BI - Helion

ebook

Autor: Jen Stirrup, Thomas J. Weinandy
ISBN: 9781098112707
stron: 472, Format: ebook
Data wydania: 2024-03-28
Księgarnia: Helion

Cena książki: 228,65 zł (poprzednio: 265,87 zł)
Oszczędzasz: 14% (-37,22 zł)

Osoby, które kupiły tę książkę, wybierały także »

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 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

Osoby które kupowały "Artificial Intelligence with Microsoft Power BI", wybierały także:

Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
Ruby on Rails. Ćwiczenia 18,75 zł, (3,00 zł -84%)
Przywództwo w świecie VUCA. Jak być skutecznym liderem w niepewnym środowisku 58,64 zł, (12,90 zł -78%)
Scrum. O zwinnym zarządzaniu projektami. Wydanie II rozszerzone 58,64 zł, (12,90 zł -78%)
Od hierarchii do turkusu, czyli jak zarządzać w XXI wieku 58,64 zł, (12,90 zł -78%)

Spis treści

Artificial Intelligence with Microsoft Power BI eBook -- spis treści

Preface
- What Is the Current State of AI Technology in Businesses?
- The Structure of This Book
- Why Did We Write This Book?
- Who Is This Book For?
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. Getting Started with AI in the Enterprise: Your Data
- Overview of Power BI Data Ingestion Methods
- Workflows in Power BI That Use AI
  - How Are Dataflows Created?
    - Creating a dataflow by importing a dataset
    - Creating a dataflow by importing/exporting a dataflow
    - Creating dataflows by defining new tables
    - Creating dataflows with linked tables
    - Creating dataflows with computed tables
    - Importing a dataflow model
    - Creating dataflows using a CDM folder
  - Things to Note Before Creating Workflows
  - Streaming Dataflows and Automatic Aggregations
- Getting Your Data Ready First
  - Getting Data Ready for Dataflows
  - Where Should the Data Be Cleaned and Prepared?
    - Option 1: Clean the data and aggregate it in the source system
    - Option 2: Clean data from a source to a secondary store
- Real-Time Data Ingestion Versus Batch Processing
  - Real-Time Datasets in Power BI
    - Setting up streaming datasets
    - Ingesting data into Power BI: Push method versus streaming method
  - Batch Processing Data Using Power BI
  - Importing Batch Data with Power Query in Dataflows
  - The Dataflow Calculation Engine
  - Dataflow Options
    - Option 1: Fully managed by Power BI
    - Option 2: Bring your own data lake
    - Option 3: External dataflows
    - Power BI dataflows in Power BI Desktop
  - DirectQuery in Power BI
  - Import Versus Direct Query: Practical Recommendations
  - Premium, Pro, and Free Power BI
- Summary
2. A Great Foundation: AI and Data Modeling
- What Is a Data Model?
  - What Is a Fact Table?
    - What is a dimension table?
    - Star and snowflake schemas
  - Why Is Data Modeling Important?
  - Why Are Data Models Important in Power BI?
  - Why Do We Need a Data Model for AI?
- Advice for Setting Up a Data Model for AI
  - Analytics Center of Excellence
  - Earning Trust Through Data Transactions
  - Agile Data Warehousing: The BEAM Framework
- Data Modeling Disciplines to Support AI
- Data Modeling Versus AI Models
  - Data Modeling in Power BI
    - Fact tables and dimension tables
    - Modeling relationships in the data model
  - What Do Relationships Mean for AI?
    - Optimizing data storage and data retrieval
    - Tips for improving performance in the data model
      - Sort columns before bringing them into a Power BI data model
      - Only bring the data that you need
      - Be mindful of bidirectional and many-to-many relationships
      - Use your own custom date table
      - Reduce data load on each page
      - Reduce the number of visuals on the page
      - Avoid iterator functions (e.g., SUMX, RANKX)
      - Optimize the data source
    - Why dont we just have one huge table?
  - Flat File Structure Versus Dimensional Model Structure in Power BI
- Summary
3. Blueprint for AI in the Enterprise
- What Is a Data Strategy?
  - Artificial Intelligence in Power BI Data Visualization
    - The Power BI decomposition tree
    - What is root cause analysis?
    - Power BI key influencers visuals
    - Q&A visual
  - Insights Using AI
  - Automated Machine Learning (AutoML) in Power BI
  - Cognitive Services
  - Data Modeling
    - Q&A
    - Quick Insights
- Real-World Problem Solving with Data
  - Binary Prediction
    - Confusion matrix
    - Accuracy
    - Per-class precision, recall, and F-1
    - Macro-averaged metrics
    - Micro-averaged metrics
    - Evaluation of highly imbalanced datasets
    - Majority class metrics
    - Random-guess metrics
  - Classification
  - Regression
    - R-squared
    - Root mean square deviation
- Practical Demonstration of Binary Prediction to Predict Income Levels
  - Gather the Data
  - Create a Workspace
  - Create a Dataflow
  - Model Evaluation Reports in Power BI
    - Accuracy report
    - Training report
- Summary
4. Automating Data Exploration and Editing
- The Transformational Power of Automation
  - Surviving (and Thriving with) Automation
    - How a data analyst can prepare
    - How a data-driven organization can prepare
  - AI Automation in Power BI
- AI in Power Query
  - Get Data from Web by Example
  - Demo 4-1: Get Data from Web by Example
  - Add Column from Examples
  - Demo 4-2: Add Column from Examples
  - Data Profiling
  - Demo 4-3: Data Profiling
  - Table Generation
  - Demo 4-4: Table Generation
  - Fuzzy Matching
  - Demo 4-5: Fuzzy Matching
- Intelligent Data Exploration
- Quick Insights
  - Demo 4-6: Quick Insights
  - Report Creation
  - Demo 4-7: Report Creation
  - Smart Narrative
- Summary
5. Working with Time Series Data
- More Than Just Timestamps
  - The Components of a Time Series
  - Changes to a Time Series
  - How Trend Lines Work in Power BI
  - Limitations of Trend Lines
  - Demo 5-1: Exploring Taxi Trip Data
- Forecasting
  - Forecasting for Business
  - How Forecasting Works
  - Limitations of Forecasting
  - Demo 5-2: Forecasting Taxi Trip Data
- Anomaly Detection
  - Anomaly Detection for Business
  - How Anomaly Detection Works
  - Limitations of Anomaly Detection
  - Demo 5-3: Anomaly Detection with Taxi Trip Data
- Summary
6. Cluster Analysis and Segmentation
- Cluster Analysis for Business
- Segmentation Meets Data Science
- Preprocessing Data for Cluster Analysis
- How Cluster Analysis Works in Power BI
  - Limitations of Cluster Analysis
  - Demo 6-1: Cluster Analysis with AirBnB Data
- Summary
7. Diving Deeper: Using Azure AI Services
- Supporting Data-Driven Decisions with a Data Dictionary
- What Is Azure AI Services?
- Accessing Azure AI Services in Power BI
  - Creating an Azure AI Services Resource
  - Creating a Power BI Report
- OpenAI ChatGPT and Power BI
  - What Is the Purpose of the Exercise?
  - Exercise Prerequisites
  - Azure OpenAI and Power BI Example
  - Generating a Secret Key and Code from the OpenAI Website
  - Creating a Streaming Power BI Dataset
  - Dashboard Didnt Work?
- Summary
8. Text Analytics
- Custom Models Versus Pretrained Models
- Text as Data
  - Limitations of Text Analytics
  - Demo 8-1: Ingest AirBnB Data
- Language Detection
  - How It Works
  - Performance and Limitations
  - Demo 8-2: Language Detection
- Key Phrase Extraction
  - How It Works
  - Performance and Limitations
  - Demo 8-3: Key Phrase Extraction
- Sentiment Analysis
  - How It Works
  - Recommendations and Limitations
  - Demo 8-4: Sentiment Analysis
  - Demo 8-5: Exploring a Report with Text Analytics
- Summary
9. Image Tagging
- Images as Data
- Deep Learning
- A Simple Neural Network
- Image Tagging for Business
  - How It Works
  - Limitations of Vision
  - Demo 9-1: Ingest AirBnB Data
  - Demo 9-2: Image Tagging
  - Demo 9-3: Exploring a Report with Vision
- Summary
10. Custom Machine Learning Models
- AI Business Strategy
  - Organizational Learning with AI
  - Successful Organizational Behaviors
- Custom Machine Learning
  - Machine Learning Versus Typical Programming
  - Narrow AI Versus General AI
- Azure Machine Learning
  - Azure Subscription and Free Trial
  - Azure Machine Learning Studio
    - 1. Home
    - 2. Notebooks
    - 3. Automated ML
    - 4. Designer
    - 5. Data
    - 6. Jobs
    - 7. Models
    - 8. Endpoints
    - 9. Compute
    - 10. Data Labeling
  - Demo 10-1: Forecasting Vending Machine Sales
    - Creating an Azure Machine Learning workspace
    - Training a custom model in Azure Machine Learning Studio
    - Deploying a custom model in Azure Machine Learning
    - Consuming a custom model in Power BI
- Summary
11. Data Science Languages: Python and R in Power BI
- Python Versus R
- Limitations
- Setup
  - Setting Up Python
  - Setting Up R
- Ingestion
  - Ingesting Data with Python
  - Ingesting Data with R
- Transformation
  - Transforming Data with Python
  - Transforming Data with R
- Visualization
  - Visualizing Data with Python
  - Visualizing Data with R
- Machine Learning
  - Using a Pretrained Model with Python on Transform
  - Training a Model with R on Ingest
- Summary
12. Making Your AI Production-Ready with Power BI
- Strategies to Help Evaluate Models
  - Scenario Without Heteroscedasticity
  - Scenario with Heteroscedasticity
  - How Does Heteroscedasticity Affect AI Models?
  - What Can Be Done If Heteroscedasticity Is Suspected?
- Making Your AI Model Ready for the Real World
  - Assessing the Costs and Benefits to the Business
  - Example ROI Calculation
- Can the Business Teams Have Confidence in the AI Model?
  - Is the Model Result Just a Fluke?
  - Assuring Ongoing Model Performance
- Making Your AI Production-Ready in Power BI
  - Data Lineage for the AI Model
  - Using the Scored Output from the Model in a Power BI Report
- Summary
13. The AI Feedback Loop
- How Do You Start the Next Project?
- How Does Feedback Affect the Training and Development of AI Models?
- AI and Edge Cases in Feedback
- How Can Feedback Help Fix Errors in an AI Model?
- AI, Bias, and Fairness
- Explainable AI and Feedback
- How Can Members of Organizations Address Ethics and AI?
  - Transfer Learning in Model Training
- How Are Other Organizations Using the AI Feedback Loop?
- How Can the AI Feedback Loop Help You?
- AI and Power BIOver to You!
Index