Stirrup Jennifer, Weinandy Thomas / Стиррап Дженнифер, Вейнанди Томас - Artificial Intelligence with Microsoft Power BI / Искусственный интеллект с помощью Microsoft Power BI [2024, PDF/EPUB, ENG]

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Оглавление

Preface ix
1. Getting Started with AI in the Enterprise: Your Data 1
Overview of Power BI Data Ingestion Methods 2
Workflows in Power BI That Use AI 3
How Are Dataflows Created? 3
Things to Note Before Creating Workflows 16
Streaming Dataflows and Automatic Aggregations 16
Getting Your Data Ready First 16
Getting Data Ready for Dataflows 16
Where Should the Data Be Cleaned and Prepared? 17
Real-Time Data Ingestion Versus Batch Processing 19
Real-Time Datasets in Power BI 19
Batch Processing Data Using Power BI 22
Importing Batch Data with Power Query in Dataflows 23
The Dataflow Calculation Engine 24
Dataflow Options 24
DirectQuery in Power BI 25
Import Versus Direct Query: Practical Recommendations 25
Premium, Pro, and Free Power BI 26
Summary 27
2. A Great Foundation: AI and Data Modeling 29
What Is a Data Model? 30
What Is a Fact Table? 30
Why Is Data Modeling Important? 31
Why Are Data Models Important in Power BI? 33
Why Do We Need a Data Model for AI? 34
Advice for Setting Up a Data Model for AI 35
Analytics Center of Excellence 35
Earning Trust Through Data Transactions 36
Agile Data Warehousing: The BEAM Framework 36
Data Modeling Disciplines to Support AI 38
Data Modeling Versus AI Models 41
Data Modeling in Power BI 41
What Do Relationships Mean for AI? 45
Flat File Structure Versus Dimensional Model Structure in Power BI 50
Summary 73
3. Blueprint for AI in the Enterprise 75
What Is a Data Strategy? 76
Artificial Intelligence in Power BI Data Visualization 78
Insights Using AI 85
Automated Machine Learning (AutoML) in Power BI 87
Cognitive Services 88
Data Modeling 88
Real-World Problem Solving with Data 89
Binary Prediction 90
Classification 93
Regression 95
Practical Demonstration of Binary Prediction to Predict Income Levels 99
Gather the Data 100
Create a Workspace 100
Create a Dataflow 100
Model Evaluation Reports in Power BI 111
Summary 115
4. Automating Data Exploration and Editing 117
The Transformational Power of Automation 117
Surviving (and Thriving with) Automation 119
AI Automation in Power BI 120
AI in Power Query 122
Get Data from Web by Example 122
Demo 4-1: Get Data from Web by Example 123
Add Column from Examples 131
Demo 4-2: Add Column from Examples 132
Data Profiling 134
Demo 4-3: Data Profiling 135
Table Generation 137
Demo 4-4: Table Generation 138
Fuzzy Matching 142
Demo 4-5: Fuzzy Matching 143
Intelligent Data Exploration 149
Quick Insights 150
Demo 4-6: Quick Insights 151
Report Creation 156
Demo 4-7: Report Creation 156
Smart Narrative 160
Summary 164
5. Working with Time Series Data 165
More Than Just Timestamps 165
The Components of a Time Series 168
Changes to a Time Series 169
How Trend Lines Work in Power BI 171
Limitations of Trend Lines 172
Demo 5-1: Exploring Taxi Trip Data 172
Forecasting 182
Forecasting for Business 183
How Forecasting Works 183
Limitations of Forecasting 184
Demo 5-2: Forecasting Taxi Trip Data 184
Anomaly Detection 187
Anomaly Detection for Business 188
How Anomaly Detection Works 188
Limitations of Anomaly Detection 189
Demo 5-3: Anomaly Detection with Taxi Trip Data 190
Summary 193
6. Cluster Analysis and Segmentation 195
Cluster Analysis for Business 195
Segmentation Meets Data Science 196
Preprocessing Data for Cluster Analysis 198
How Cluster Analysis Works in Power BI 200
Limitations of Cluster Analysis 201
Demo 6-1: Cluster Analysis with AirBnB Data 201
Summary 211
7. Diving Deeper: Using Azure AI Services 213
Supporting Data-Driven Decisions with a Data Dictionary 214
What Is Azure AI Services? 215
Accessing Azure AI Services in Power BI 216
Creating an Azure AI Services Resource 216
Creating a Power BI Report 220
OpenAI ChatGPT and Power BI 220
What Is the Purpose of the Exercise? 220
Exercise Prerequisites 221
Azure OpenAI and Power BI Example 221
Generating a Secret Key and Code from the OpenAI Website 224
Creating a Streaming Power BI Dataset 229
Dashboard Didn’t Work? 258
Summary 259
8. Text Analytics 261
Custom Models Versus Pretrained Models 262
Text as Data 263
Limitations of Text Analytics 264
Demo 8-1: Ingest AirBnB Data 265
Language Detection 270
How It Works 270
Performance and Limitations 270
Demo 8-2: Language Detection 271
Key Phrase Extraction 276
How It Works 277
Performance and Limitations 278
Demo 8-3: Key Phrase Extraction 278
Sentiment Analysis 282
How It Works 283
Recommendations and Limitations 283
Demo 8-4: Sentiment Analysis 284
Demo 8-5: Exploring a Report with Text Analytics 290
Summary 292
9. Image Tagging 293
Images as Data 293
Deep Learning 295
A Simple Neural Network 296
Image Tagging for Business 299
How It Works 300
Limitations of Vision 302
Demo 9-1: Ingest AirBnB Data 303
Demo 9-2: Image Tagging 308
Demo 9-3: Exploring a Report with Vision 314
Summary 319
10. Custom Machine Learning Models 321
AI Business Strategy 321
Organizational Learning with AI 322
Successful Organizational Behaviors 324
Custom Machine Learning 324
Machine Learning Versus Typical Programming 325
Narrow AI Versus General AI 326
Azure Machine Learning 328
Azure Subscription and Free Trial 330
Azure Machine Learning Studio 330
Demo 10-1: Forecasting Vending Machine Sales 337
Summary 359
11. Data Science Languages: Python and R in Power BI 361
Python Versus R 363
Limitations 365
Setup 365
Setting Up Python 366
Setting Up R 372
Ingestion 374
Ingesting Data with Python 375
Ingesting Data with R 378
Transformation 380
Transforming Data with Python 381
Transforming Data with R 384
Visualization 386
Visualizing Data with Python 386
Visualizing Data with R 390
Machine Learning 393
Using a Pretrained Model with Python on Transform 394
Training a Model with R on Ingest 398
Summary 401
12. Making Your AI Production-Ready with Power BI 403
Strategies to Help Evaluate Models 404
Scenario Without Heteroscedasticity 404
Scenario with Heteroscedasticity 404
How Does Heteroscedasticity Affect AI Models? 405
What Can Be Done If Heteroscedasticity Is Suspected? 405
Making Your AI Model Ready for the Real World 406
Assessing the Costs and Benefits to the Business 407
Example ROI Calculation 409
Can the Business Teams Have Confidence in the AI Model? 411
Is the Model Result Just a Fluke? 411
Assuring Ongoing Model Performance 412
Making Your AI Production-Ready in Power BI 413
Data Lineage for the AI Model 420
Using the Scored Output from the Model in a Power BI Report 420
Summary 420
13. The AI Feedback Loop 423
How Do You Start the Next Project? 423
How Does Feedback Affect the Training and Development of AI Models? 424
AI and Edge Cases in Feedback 424
How Can Feedback Help Fix Errors in an AI Model? 426
AI, Bias, and Fairness 426
Explainable AI and Feedback 428
How Can Members of Organizations Address Ethics and AI? 428
Transfer Learning in Model Training 431
How Are Other Organizations Using the AI Feedback Loop? 432
How Can the AI Feedback Loop Help You? 433
AI and Power BI—Over to You! 434
Index 437