Inhaltsverzeichnis

Alle Kapitel aufklappen
Alle Kapitel zuklappen
Preface
15
Who Should Read this Book
15
Structure of the Book
16
Part I: Introduction
16
Part II: Building Machine Learning Applications
17
Part III: Use Cases and Roadmaps
18
Acknowledgments
18
Laboni Bhowmik
18
Avijit Dhar
18
Ranajay Mukherjee
18
PART I Introduction
21
1 Machine Learning and Intelligent Enterprise
23
1.1 What Is Machine Learning?
25
1.2 Transition from the Digital Era to the Intelligent Era
25
1.3 Intelligent Enterprise Use Cases
26
1.3.1 Intelligent Invoice Matching
26
1.3.2 Smart Recruiting
27
1.3.3 Service Ticket Intelligence
27
1.3.4 Product Similarity Matching
27
1.3.5 Brand Impact
28
1.3.6 Robotic Process Automation
28
1.3.7 Virtual Assistance
29
1.4 SAP’s Intelligent Enterprise Strategy
29
1.4.1 Intelligent Suite
30
1.4.2 Digital Platform
31
1.4.3 Intelligent Technologies
31
1.5 SAP’s Machine Learning Technologies and Applications
32
1.5.1 Embedded Machine Learning Applications
32
1.5.2 SAP Intelligent Robotic Process Automation
33
1.5.3 SAP Conversational Artificial Intelligence
34
1.5.4 SAP Data Intelligence
34
1.5.5 Embedded Machine Learning within SAP HANA
35
1.6 Summary
36
2 Machine Learning Fundamentals
37
2.1 Basic Probability Concepts
37
2.1.1 What is Probability?
38
2.1.2 Conditional Probability and Independence
47
2.1.3 Random Variables
49
2.1.4 Density and Distribution Function
54
2.1.5 Expectation
60
2.1.6 Variance and Standard Deviation
61
2.1.7 Covariance and Correlation
62
2.2 Basic Machine Learning Concepts
63
2.2.1 Different Types of Machine Learning Scenarios
63
2.2.2 Types of Learning
65
2.3 Machine Learning Algorithms
66
2.3.1 Linear Regression Analysis
66
2.3.2 Classification
85
2.3.3 Cluster Analysis
113
2.3.4 Other Machine Learning Techniques
125
2.4 Summary
137
3 Implementation Lifecycle
139
3.1 Understanding the Implementation Lifecycle
140
3.1.1 Cross Industry Standard Process for Data Mining
140
3.1.2 Define, Measure, Analyze, Improve, Control
141
3.1.3 Analytics Solutions Unified Method for Data Mining/Predictive Analytics
142
3.1.4 Key Components in a Machine Learning Project Lifecycle
142
3.2 Knowing the Business
143
3.3 Understanding and Exploring Data
144
3.3.1 Checking Data Quality
147
3.3.2 Checking Summary Statistics
151
3.3.3 Visualizing Data
153
3.4 Preparing Data
156
3.4.1 Data Conversion
157
3.4.2 Dimensionality Reduction
160
3.4.3 Variable Transformation
161
3.5 Developing the Model
163
3.6 Evaluating and Fine-Tuning Model
165
3.6.1 Model Overfitting
165
3.6.2 Model Performance
166
3.6.3 Model Fine-Tuning
168
3.6.4 Diagnostic Checks
168
3.6.5 Validation Set Approach
170
3.6.6 K-Fold Cross Validation
171
3.7 Deploying the Model
172
3.8 Summary
173
4 Machine Learning on SAP HANA
175
4.1 SAP HANA Machine Learning Components
175
4.1.1 SAP HANA Predictive Analysis Library
177
4.1.2 SAP HANA Automated Predictive Library
181
4.1.3 SAP HANA Extended Machine Learning Library
184
4.1.4 R Integration
188
4.2 Summary
204
5 Machine Learning with SAP Data Intelligence
205
5.1 Data Science Project Lifecycle
207
5.2 Managing the Data Science Project Lifecycle
209
5.3 SAP Data Intelligence
210
5.3.1 SAP Data Intelligence Versus SAP Data Hub
211
5.3.2 Architecture
212
5.3.3 Values of SAP Data Intelligence
215
5.4 Key Capabilities
216
5.4.1 Metadata and Governance
217
5.4.2 Machine Learning Scenario Manager
218
5.4.3 Machine Learning Data Manager
220
5.4.4 AutoML
221
5.4.5 Machine Learning Tracking
223
5.4.6 SAP Data Intelligence Modeler and Built-in Operators
227
5.5 Migrating to SAP Data Intelligence from SAP Data Hub
235
5.6 Summary
236
PART II Building Machine Learning Applications
239
6 SAP HANA Predictive Analysis Library and R Integration
241
6.1 SAP HANA Predictive Analysis Library
241
6.1.1 Prerequisites and Installation
241
6.1.2 SAP HANA PAL Procedures
243
6.1.3 Calling SAP HANA PAL Procedures
250
6.1.4 Model Evaluation
260
6.2 R Integration
266
6.2.1 Prerequisites and Installation
266
6.2.2 R Packages
267
6.2.3 Calling R Functions from SAP HANA
268
6.2.4 Model Evaluation
275
6.3 Summary
278
7 Developing Applications with SAP HANA Predictive Analysis Library
279
7.1 Introduction to the Use Case
279
7.2 Building a Predictive Analytics Application Using SAP HANA PAL
280
7.2.1 Understanding the Business Problem and the Data
281
7.2.2 Data Preparation
291
7.2.3 Model Building
293
7.2.4 Model Validation and Refinement
300
7.2.5 Model Deployment
307
7.2.6 Interpretation and Displaying Results
311
7.3 Summary
315
8 SAP AI Business Services
317
8.1 Overview
318
8.2 Document Classification
319
8.2.1 Test Run of the Document Classification Service
322
8.3 Document Information Extraction
332
8.3.1 Test Run of the Document Information Extraction Service
335
8.4 Business Entity Recognition
339
8.5 Data Attribute Recommendation
341
8.5.1 Test Run of the Data Attribute Recommendation Service
344
8.6 Invoice Object Recommendation
347
8.7 SAP Service Ticket Intelligence
348
8.7.1 Test Run of the Service Ticket Intelligence Service
350
8.8 Summary
352
9 Building Scenarios Using Jupyter Notebook
353
9.1 Adding a Notebook
354
9.2 SAP Data Intelligence Python SDK
357
9.2.1 Setting Up and Leveraging the SAP Data Intelligence SDK
358
9.2.2 SAP Data Intelligence Python SDK
358
9.2.3 Popular Python Libraries
359
9.2.4 Various Library Options
360
9.3 Use Case
361
9.3.1 Setting Up the Scenario
363
9.3.2 Scripting
367
9.3.3 Data Plotting
371
9.3.4 Code Execution
372
9.4 Summary
374
10 Automated Machine Learning Data Science Automation
375
10.1 AutoML on SAP Data Intelligence
376
10.2 Features of AutoML
376
10.3 AutoML Step-by-Step
377
10.3.1 Configuring the Training Data Collection
378
10.3.2 Configuring the Test Data Collection
384
10.3.3 Setting Up AutoML
385
10.3.4 Testing AutoML
395
10.4 Summary
397
11 Conversational Artificial Intelligence
399
11.1 Introduction to SAP Conversational Artificial Intelligence
399
11.2 SAP Conversational AI
401
11.2.1 Natural Language Processing Engine
402
11.2.2 Intents
404
11.2.3 Expressions
405
11.2.4 Entities
406
11.2.5 Languages
409
11.2.6 Channels
412
11.3 Bot Building Techniques
412
11.3.1 Bot Building Process
413
11.3.2 Bot Connector
417
11.3.3 Bot Monitoring and Analytics
418
11.3.4 SAP Conversational AI APIs
419
11.4 Building a Chatbot Using SAP Conversational AI
421
11.4.1 Let’s Build a Bot!
423
11.4.2 Setting Up the Bot Project
424
11.4.3 Creating Intents
425
11.4.4 Setting Up Entities
427
11.4.5 Setting Up Skills
429
11.4.6 Enabling Voice
435
11.5 Summary
436
PART III Use Cases and Roadmaps
437
12 Integrating Machine Learning with the Internet of Things and Blockchain
439
12.1 Technology-Driven Transformation
441
12.2 Data—The Common Theme
442
12.3 Use Cases
445
12.3.1 Supply Chain
445
12.3.2 Healthcare
450
12.4 Summary
456
13 Industry Use Cases for Machine Learning Applications
457
13.1 Acceptance of Machine Learning across Different Industries
457
13.1.1 Assessing the Impact of Machine Learning
458
13.1.2 Challenges of Machine Learning Applications
460
13.2 Machine Learning Ecosystem
461
13.3 Identifying Industry Use Cases
464
13.3.1 Manufacturing Industry
465
13.3.2 Customer Service Industry
469
13.3.3 Healthcare Service Industry
473
13.3.4 Finance Industry
476
13.4 Summary
480
14 Conclusion and Roadmap
481
14.1 Recap
481
14.2 Best Practices
483
14.3 Roadmap
484
14.4 Summary
486
The Authors
487
Index
489