Machine Learning with SAP: Models and Applications (Hardcover)

... Preface ... 15

... Who Should Read this Book ... 15

... Structure of the Book ... 16

... Acknowledgments ... 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

1.4 ... SAP's Intelligent Enterprise Strategy ... 29

1.5 ... SAP's Machine Learning Technologies and Applications ... 32

1.6 ... Summary ... 36

2 ... Machine Learning Fundamentals ... 37

2.1 ... Basic Probability Concepts ... 37

2.2 ... Basic Machine Learning Concepts ... 63

2.3 ... Machine Learning Algorithms ... 66

2.4 ... Summary ... 137

3 ... Implementation Lifecycle ... 139

3.1 ... Understanding the Implementation Lifecycle ... 140

3.2 ... Knowing the Business ... 143

3.3 ... Understanding and Exploring Data ... 144

3.4 ... Preparing Data ... 156

3.5 ... Developing the Model ... 163

3.6 ... Evaluating and Fine-Tuning Model ... 165

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

4.2 ... 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

5.4 ... Key Capabilities ... 216

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

6.2 ... R Integration ... 266

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

7.3 ... Summary ... 315

8 ... SAP AI Business Services ... 317

8.1 ... Overview ... 318

8.2 ... Document Classification ... 319

8.3 ... Document Information Extraction ... 332

8.4 ... Business Entity Recognition ... 339

8.5 ... Data Attribute Recommendation ... 341

8.6 ... Invoice Object Recommendation ... 347

8.7 ... SAP Service Ticket Intelligence ... 348

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

9.3 ... Use Case ... 361

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

10.4 ... Summary ... 397

11 ... Conversational Artificial Intelligence ... 399

11.1 ... Introduction to SAP Conversational Artificial Intelligence ... 399

11.2 ... SAP Conversational AI ... 401

11.3 ... Bot Building Techniques ... 412

11.4 ... Building a Chatbot Using SAP Conversational AI ... 421

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

12.4 ... Summary ... 456

13 ... Industry Use Cases for Machine Learning Applications ... 457

13.1 ... Acceptance of Machine Learning across Different Industries ... 457

13.2 ... Machine Learning Ecosystem ... 461

13.3 ... Identifying Industry Use Cases ... 464

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