Bhowmik, L: Machine Learning with SAP

1. Auflage 2020
ISBN: 978-1-4932-1926-1
Verlag: Rheinwerk Verlag GmbH

Buch, Englisch, 495 Seiten, Format (B × H): 185 mm x 254 mm, Gewicht: 1168 g

Reihe: SAP PRESS: englisch

Work smarter with machine learning! Begin with core machine learning concepts—types of learning, algorithms, data preparation, and more. Then use SAP Data Intelligence, SAP HANA, and other technologies to create your own machine learning applications. Master the SAP HANA Predictive Analysis Library (PAL) and machine learning functional and business services to train and deploy models. Finally, see machine learning in action in industries from manufacturing to banking.

a. Foundation
Build your understanding of probability concepts and algorithms that drive machine learning. See how linear regression, classification, and cluster analysis algorithms work, before plugging them into your very own machine learning app!
b. Development
Follow step-by-step instructions to gather and prepare data, create machine learning models, train and fine-tune models, and deploy your final app, all using SAP HANA and SAP Data Intelligence.
c. Platforms
Use built-in SAP HANA libraries to create applications that consume machine learning algorithms or integrate with the R language for additional statistical capabilities. Work with the SAP Leonardo functional services to customize and embed pre-trained models into applications or bring your own model with the help of Google TensorFlow.

1) Development
2) Retraining
3) Implementation
4) SAP Data Intelligence
5) SAP HANA predictive analysis library
6) SAP HANA extended machine learning library
7) SAP HANA automated predictive library
8) Google TensorFlow
9) Embedded machine learning
10) SAP Conversational AI
11) SAP Analytics Cloud Smart Predict

Autoren/Hrsg.

Bhowmik, Laboni

Dhar, Avijit

Mukherjee, Ranajay

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware SAP
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

... 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