SAP Leonardo Machine Learning - Making Business Applications Intelligent

1. SAP Leonardo Machine Learning Making Business Applications Intelligent Nazanin Zaker, Lead Data Scientist, SAP Machine Learning Business Network Frank Wu, Head of SAP Machine Learning Business Network CUSTOMER

2. Legal disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation and SAP's strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this document is not a commitment, promise or legal obligation to deliver any material, code or functionality. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP´s willful misconduct or gross negligence. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. 2 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

3. Machine learning is the reality behind artificial intelligence What is machine learning? § Computers learn from data without being explicitly programmed. Why now? § Machines can see, read, listen, understand, and interact. § Big Data (for example, business networks, cloud applications, the Internet of Things, and SAP S/4HANA) § Massive improvements in hardware (graphics processing unit [GPU] and multicore) § Deep learning algorithms 3 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

4. The Automation of Repetitive Tasks is Allowing Humans to be More Productive and Focus on Higher Value Tasks 60% Of human tasks will be automated by 2025 Productivity 94% 97% Image recognition accuracy (human: 95%) 95% Speech recognition accuracy (human: 94%) Human high value tasks (augmented by AI) Of companies see ML as critical for competitive advantage $18B Enterprise Machine Learning Market by 2020 Human repetitive tasks Enterprise system Digital Enterprise Transactional Enterprise Intelligent Enterprise 4 Source: SAP CSG analysis, McKinesy Quarterly Report July 2016, Google PR, Microsoft PR, SAP Market Model © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

5. SAP Leonardo Enables the Intelligent Enterprise SAP Leonardo SAP Leonardo Business Outcomes Machine Learning Increase revenue 76% of the world’s transaction revenue touches an SAP system Conversational Interfaces Re-imagine processes Intelligent Apps 25 industries Intelligent Cloud Intelligent S/4HANA Quality time at work 12 lines of business Data Science Platform Customer satisfaction Intelligent Services The world’s largest business network Enabling innovations 5 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

6. SAP Leonardo Machine Learning: transforming enterprise data into business value Input Machine Learning Output Train model Applications (such as cash application) Video Text Prepare data Apply model Speech Image …and more … and more Services Capture feedback (such as invoice processing, profile matching) 6 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

7. SAP Leonardo Machine Learning: Portfolio of Capabilities Conversational Interfaces End-User Intelligent Apps Integration of Machine Learning into existing applications (e.g. SAP Analytics Cloud, SAP Business Integrity Screening, SAP Cash Application) Standalone Machine Learning Applications (e.g. SAP Brand Impact) Intelligent Services Data Science Platform & Tools Text/ Document Services (e.g. Sentiment Analysis) Image/Video Services (e.g. Image Classification) Speech/ Audio Services (e.g. Voice Recognition) Predictive Services (e.g. Forecasting) Data Exploration In-Application Deployment Model Storage Data Scientist Developer Production Readiness Data Integration Lifecycle Management Structured Data Services (e.g. Time Series Analysis) Graph Services (e.g. Link Recommender) Business Services (e.g. Service Ticket Intelligence) Data Preparation TensorFlow Integration ML Model Creation End to End Automation SAP Cloud Platform SAP HANA Platform 7 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

8. Reimagine your value chain with SAP Leonardo Machine Learning Design Inbound Logistics Trend Analysis (Face, Age, Gender, Emotion, Apparel) • Image-based Purchasing • Goods & Services Classification • Personalized Design • Supplier Risk Assessment • Marketing Sales & Service Catalog Enrichment • Conversational AI Brand Impact Operations • • Outbound Logistics Service Ticketing Social Media Analysis • • Predictive Maintenance • Routing Optimization • Customer Support Customer Behavior Segmentation • • Quality Inspection • Supply Chain Resilience • Solution Recommender • Optimal Planning & Scheduling • Last-mile Delivery • Cash Application • Learning Recommender • Accounts Payable • Synchronous Translation of training content • Remittance Advices • Predictive Accounting Career Path Recommender • • SAP Business Integrity Screening Finance • Human Resources 8 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

9. SAP Leonardo Machine Learning Strategic Partnerships Partners Focus Areas Achievements § First Enterprise offering to use NVIDIA's Volta AI Platform § Running Kubernetes on NVIDIA GPUs in SAP Data Center § Open-source software library for Machine Intelligence § Our standard ML framework (ease of training, enablement) Study & formulate best practices on AI tech, Advance the public’s understanding of AI, Serve as an open platform for discussion and engagement about AI, and its influences on people and society § § SAP accepted as partner § § Enables one global answer to ML & AI ethics § § 9 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

10. SAP Cash Application Next-generation intelligent invoice-matching powered by machine-learning History Payments Invoices Matching proposals SAP Leonardo SAP Leonardo Machine Learning SAP Cash Application intelligently learns matching criteria from your history and automatically clears payments. Integrated with SAP S/4HANA for reduced TCO and time to value Allows shared services to scale as the business grows Empowers finance to focus on strategic tasks and service quality Improves days sales outstanding 10 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

11. SAP Cash Application Next-generation intelligent invoice matching powered by machine learning 11 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

12. SAP Leonardo Machine Learning Foundation Enabling customers and partners to build the intelligent enterprise SAP Leonardo Machine Learning Foundation Ready to use Services Inference Bring your own Model Applications Ready to use Customize Model Training Create Training SAP Cloud Platform 12 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

13. SAP Leonardo Machine Learning Foundation Core capabilities - description of features Ready to use services provides readily consumable pre-trained models that can be used as a web service by calling simple REST APIs Explore the functional services such as image classification, product image classification, topic detection, time series changepoint detection • • Bring your own model Deploy and run your own TensorFlow Model on ML foundation Manage your model’s status and monitor its resource consumption Leverage and benefit from the platform capabilities of ML foundation like authentication and scalability • • • Customize model Use your existing data assets to retrain ML foundation’s image or text classifier Simply access ML foundation’s API for retraining – no extensive machine learning knowledge required Leverage ML foundation’s capabilities to serve your training jobs • • • 13 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

14. SAP Leonardo Machine Learning Foundation Broken product similarity search use case SAP Leonardo SAP Leonardo Machine Learning Product Identification and automatic classification Service Ticket, e-mail incl. image of broken product Image Feature Extraction Similarity Scoring SAP’s Machine Learning automatically classifies product images and enables faster customer interaction with precise information on potential product repair cost or item substitution. 14 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

15. The combination of Functional Services Broken product similarity search use case Image Feature Extraction Service Vectors DB Images DB Result Image Feature Extraction Service Similarity Scoring 15 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

16. SAP Leonardo Machine Learning Foundation Broken product similarity search demo 16 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

17. SAP Leonardo Machine Learning Foundation Ready-to-use Services: Roadmap Status as of October 2017 Tabular Image & Video Text Speech & Audio Business Services § Time series change point detection § Similarity scoring § Image classification § Customizable image classification § Image feature extraction § Topic detection § Text classification § Text feature extraction § Customizable text classification § Intelligent Financing API § Ticket Intelligence - Classification § Ticket Intelligence - Recommendation General availability § Multi-dimensional time series forecasting § Product image classification § Human detection service § Object detection service § Machine translation § Language detection § Product text classification § Document clustering § Speech-to-text* Alpha § Time-to-failure forecasting § Association rule learning § Customizable recommender § Multi-dimensional data clustering § Generic classification (tabular and text) § Image segmentation § Face detection § Document optical character recognition § Image text extraction § Image NER/extraction § Apparel detection § Sentiment analysis § Named entity recognition § Hate speech detection § File-to-text conversion § Voice recognition (speaker identification) § Text-to-speech* § CV Matching § Customer Retention § Brand Impact § Accounts Payable Road map *Internal release only, not yet available for externals 17 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

18. Ready-to-use Services: Easy Consumption Calling REST APIS through the API Business Hub 18 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

19. SAP Leonardo Machine Learning Foundation Release Plan ___________Preparation___________ ____________Training_____________ _____Usage_____ ____________Inference____________ Training Execution Model Publishing Service Consumption Integrated ML Capabilities Data Preparation Training Creation Data Exploration Consume scalable and secure ML Services on SAP Cloud Platform. Use ML capabilities integrated in SAP solutions. Clean and label your data. Configure existing models with your own data. Deploy your model and make it available as a service. Upload your Training-Script and your data to create your own model. Explore and analyse your data. Bring your own Model Ready to use Services Ready to use Apps available Customize Model newly released roadmap Prepare your data Train your own model 19 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

20. SAP Catalog Enrichment LaserJet Laser Printer - Plain Paper Print M506DN - Plain paper LaserJet Printer - Multi-level device security helps protect from threats -Original HP Toner cartridges with JetIntelligence and this printer produce more high-quality pages. -11.7? H x 16.5? W x 14.8? D -Media Feeder -1 x automatic - 100 sheets - Legal (8.5 in x 14 in) weight: 60 g/m2 - 199 g/m2 - 1 x automatic - 550 sheets - Legal (8.5 in x 14 in) weight: 60 g/m2 - 120 g/m2 Import Train Model Clean & Prepare Get Data HP Brand Part ID M506d Model Supplier Laser Technology Test Model Name 11.7x16.5x14.8 Dimension Export Description Color Output Released last week 20 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

21. Catalog Enrichment Demo 21 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

22. Integration Catalog Management Application System integration Product Description Normalized Attributes / Items SAP Catalog Enrichment Service on SAP Leonardo ML Foundation SAP Cloud Platform 22 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

23. Information Extraction in SAP Catalogs What are Information extraction systems: • Collect information from many parts of text, and understand limited relevant pieces. • Create a structured representation of relevant information. Goals Organize information and make it practical for the users: as an example, Table format catalogs Put information in a new form that allows further functions to be made by computer algorithms on top of them: as an example, Make catalogs searchable Ÿ Ÿ Solution • Named Entity Recognition (NER): It is a sub task to find and classify names in text. 23 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

24. Three standard approaches to NER q Rule Based NER q Supervised Sequence models q Unsupervised models q Semi-supervised learning 24 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

25. Rule Based NER q Create regular expressions to extract entities. q Provide a flexible way to match strings of text. Example: Suppose you are looking for a word that: 1. 2. 3. 4. 5. 6. starts with a capital letter “N” is the first word on a line the second 2 letters are lower case letter is exactly 5 letters long the 4th letter is a vowel The last letter is lower case the regular expression would be “^N[a-z][a-z][aeiou][a-z]” where 25 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

26. Simple methods will not always work! q Capitalization is a strong indicator for capturing proper names, but it can be tricky: § First word of a sentence, titles, nested named entities are capitalized q New proper names constantly emerge movie titles, books, singers, restaurants, and etc. q The same entity can have multiple variants of the same proper name q Proper names are ambiguous 26 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

27. Learning System q Supervised learning § labeled training examples § methods: Hidden Markov Models, k-Nearest Neighbors, Decision Trees, AdaBoost, SVM, RNNs (LSTM, BiLSTM) § examples: NE recognition, POS tagging, Parsing q Unsupervised learning § labels must be automatically discovered § method: clustering § examples: NE disambiguation, text classification q Semi-supervised learning § small percentage of training examples are labeled, the rest is unlabeled § methods: bootstrapping, active learning, co-training, self-training § examples: NE recognition, POS tagging, Parsing, … 27 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

28. The ML sequence model approach to NER Training Collect a set of representative training documents 1. Label each token for its entity class or other (NA) 2. Design feature extractors appropriate to the text and classes 3. Train a sequence classifier to predict the labels from the data 4. Testing Receive a set of testing documents 1. Run sequence model inference to label each token 2. Appropriately output the recognized entities 3. 28 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

29. Conditional Random Fields - sequence model: Conditional Random Fields (CRFs) - It is a complete sequence conditional model, and not only a chaining of local models. Training is slower comparing to hidden Markov models (HMM). CRFs are very similar to maximum entropy Markov models (MEMMs). 29 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

30. Named Entity Recognition using multi-layered bidirectional LSTMs Sentences are used as inputs for the recurrent neural network. Representation of words in the sentence is via the form of embedding. § Possible embedding: word2vec, Glove, fasttext Bidirectional LSTM network are used to classify the named entities. § 2 layers of bidirectional network § Softmax as the last layer to produce the final classification outputs. § AdamOptimzer for optimization Evaluation: § F1 Scores, Prediction Accuracy and Recall 30 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

31. Named Entity Recognition using multi-layered bidirectional LSTMs (Cont.) 64gb 10 microsdxc card class Tokenizing Stemming Word2ve c model Word vectors Word2vec/Glove Bi- LSTM Bi- LSTM Training (BiLSTM) Embeddin g For words Test and get accuracy Softmax capacity transmission type type transmission speed speed 31 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

32. Examples and Detection Accuracy Ø Sample text inputs and the classification results for SAP Catalogs. Sample product description #1: macbook pro 13in - space gray: 2.3ghz 256gb (mpxt2ll/a + s6202ll/a) sea # 1735383 quote # 2204065820 - 2.3ghz dual-core intel core i5 turbo boost up to 3.6ghz / intel iris plus graphics 640 / 8gb 2133mhz lpddr3 sdram / 256gb pcie- based ssd / force touch trackpad / two thunderbolt 3 ports / backlit keyboard (english) / user's guide (english) / applecare+ for 13-inch macbook pro mpxt2ll/a + s6202ll/a Sample product description #2: c560 23-inch ultraslim notebook c560 ultraslim notebook - 3.5 ghz intel core 6gb ddr3 2.5tb hdd 32gb ssd windows 7 hd capacity: memory (ram): operational system: type: processor: 2.5tb hdd 32gb ssd 6gb ddr3 windows 7 c560 ultraslim notebook 3.5 ghz intel core hd capacity: memory (ram): color: video card: type: 256gb 8gb 2133mhz lpddr3 sdram space gray iris plus graphics 640 macbook pro 2.3ghz dual core intel core i5 turbo boost up to 3.6ghz intel Results (accuracy: 86%) Positive Negative True 344 63 False 35 276 processor: 32 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ CUSTOMER

33. Questions?

34. SAP Leonardo Machine Learning Machine Learning Thank you