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SAP Leonardo Machine Learning - Making Business Applications Intelligent

See how SAP Leonardo is enabling the development of more intelligent enterprises with the power of machine learning, big data, and NVIDIA GPUs.

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SAP Leonardo Machine Learning - Making Business Applications Intelligent

  1. 1. CUSTOMER Nazanin Zaker, Lead Data Scientist, SAP Machine Learning Business Network Frank Wu, Head of SAP Machine Learning Business Network SAP Leonardo Machine Learning Making Business Applications Intelligent
  2. 2. 2CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 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. Legal disclaimer
  3. 3. 3CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Machine learning is the reality behind artificial intelligence § 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 § Computers learn from data without being explicitly programmed. § Machines can see, read, listen, understand, and interact. What is machine learning? Why now?
  4. 4. 4CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Source: SAP CSG analysis, McKinesy Quarterly Report July 2016, Google PR, Microsoft PR, SAP Market Model 60% Of companies see ML as critical for competitive advantage $18B Enterprise Machine Learning Market by 2020 94% Transactional Enterprise Digital Enterprise Intelligent Enterprise Of human tasks will be automated by 2025 97% Image recognition accuracy (human: 95%) 95% Speech recognition accuracy (human: 94%) Productivity Human repetitive tasks Enterprise system Human high value tasks (augmented by AI) The Automation of Repetitive Tasks is Allowing Humans to be More Productive and Focus on Higher Value Tasks
  5. 5. 5CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 76% of the world’s transaction revenue touches an SAP system 25 industries 12 lines of business Data Science Platform Intelligent Services Intelligent Apps Conversational Interfaces Machine Learning SAP Leonardo Business Outcomes Increase revenue Re-imagine processes Quality time at work Customer satisfaction Enabling innovations SAP Leonardo Enables the Intelligent Enterprise Intelligent S/4HANA Intelligent Cloud The world’s largest business network
  6. 6. 6CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning: transforming enterprise data into business value Input Machine Learning Output Train model Prepare data Apply model Capture feedback Text Image Video Speech … and more Services (such as invoice processing, profile matching) …and more Applications (such as cash application)
  7. 7. 7CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning: Portfolio of Capabilities Data Science Platform & Tools Developer DataScientist Text/ Document Services (e.g. Sentiment Analysis) Image/Video Services (e.g. Image Classification) Speech/ Audio Services (e.g. Voice Recognition) Structured Data Services (e.g. Time Series Analysis) Business Services (e.g. Service Ticket Intelligence) Graph Services (e.g. Link Recommender) Predictive Services (e.g. Forecasting) Intelligent Services Data Exploration Data Integration Data Preparation End to End Automation In-Application Deployment Lifecycle Management ML Model Creation Model Storage Production Readiness TensorFlow Integration 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 Apps Conversational Interfaces SAP Cloud Platform SAP HANA Platform End-User
  8. 8. 8CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Reimagine your value chain with SAP Leonardo Machine Learning • Trend Analysis (Face, Age, Gender, Emotion, Apparel) • Personalized Design Design • Learning Recommender • Synchronous Translation of training content • Career Path Recommender Human Resources • Predictive Maintenance • Quality Inspection • Optimal Planning & Scheduling Operations • Cash Application • Accounts Payable • Remittance Advices • Predictive Accounting • SAP Business Integrity Screening Finance • Image-based Purchasing • Goods & Services Classification • Supplier Risk Assessment • Catalog Enrichment Inbound Logistics • Routing Optimization • Supply Chain Resilience • Last-mile Delivery Outbound Logistics • Brand Impact • Social Media Analysis • Customer Behavior Segmentation Marketing • Conversational AI • Service Ticketing • Customer Support • Solution Recommender Sales & Service
  9. 9. 9CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning Strategic Partnerships § 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 § 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) Partners Focus Areas Achievements
  10. 10. 10CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Cash Application Next-generation intelligent invoice-matching powered by machine-learning History Payments Invoices Matching proposals Improves days sales outstanding Allows shared services to scale as the business grows Integrated with SAP S/4HANA for reduced TCO and time to value Empowers finance to focus on strategic tasks and service quality SAP Cash Application intelligently learns matching criteria from your history and automatically clears payments. SAP Leonardo Machine Learning
  11. 11. 11CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Cash Application Next-generation intelligent invoice matching powered by machine learning
  12. 12. 12CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning Foundation Enabling customers and partners to build the intelligent enterprise Applications Ready to use Training Inference SAP Leonardo Machine Learning Foundation Ready to use Services Bring your own Model Customize Model Create Training SAP Cloud Platform
  13. 13. 13CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning Foundation Core capabilities - description of features • 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 Bring your own 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 Customize model 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
  14. 14. 14CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning Foundation Broken product similarity search use case Image Feature Extraction Similarity Scoring Service Ticket, e-mail incl. image of broken product Product Identification and automatic classification SAP’s Machine Learning automatically classifies product images and enables faster customer interaction with precise information on potential product repair cost or item substitution. SAP Leonardo Machine Learning
  15. 15. 15CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ The combination of Functional Services Broken product similarity search use case Images DB Image Feature Extraction Service Vectors DB Image Feature Extraction Service Similarity Scoring Result
  16. 16. 16CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning Foundation Broken product similarity search demo
  17. 17. 17CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning Foundation Ready-to-use Services: Roadmap Tabular Image & Video Text Speech & Audio Business Services General availability § 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 Alpha § 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* Road map § 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 *Internal release only, not yet available for externals Status as of October 2017
  18. 18. 18CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Ready-to-use Services: Easy Consumption Calling REST APIS through the API Business Hub
  19. 19. 19CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ SAP Leonardo Machine Learning Foundation Release Plan ___________Preparation___________ ____________Training_____________ available newly released roadmap ____________Inference____________ _____Usage_____ Training Execution Model Publishing Service Consumption Integrated ML Capabilities Configure existing models with your own data. Deploy your model and make it available as a service. Consume scalable and secure ML Services on SAP Cloud Platform. Use ML capabilities integrated in SAP solutions. Customize Model Ready to use Apps Ready to use Services Bring your own Model Training Creation Train your own model Upload your Training-Script and your data to create your own model. Data PreparationData Exploration Clean and label your data. Explore and analyse your data. Prepare your data
  20. 20. 20CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Clean & Prepare Train Model Test Model Get Data Part ID Supplier Name Description 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 Export Brand Model Technology Dimension Output HP M506d Laser 11.7x16.5x14.8 Color SAP Catalog Enrichment Released last week
  21. 21. 21CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Catalog Enrichment Demo
  22. 22. 22CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Integration SAP Cloud Platform Catalog Management Application System integration Product Description Normalized Attributes / Items SAP Catalog Enrichment Service on SAP Leonardo ML Foundation
  23. 23. 23CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 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. Ÿ Organize information and make it practical for the users: as an example, Table format catalogs Ÿ 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 • Named Entity Recognition (NER): It is a sub task to find and classify names in text. Goals Solution
  24. 24. 24CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Three standard approaches to NER q Rule Based NER q Supervised Sequence models q Unsupervised models q Semi-supervised learning
  25. 25. 25CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 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. starts with a capital letter “N” 2. is the first word on a line 3. the second 2 letters are lower case letter 4. is exactly 5 letters long 5. the 4th letter is a vowel 6. The last letter is lower case the regular expression would be “^N[a-z][a-z][aeiou][a-z]” where
  26. 26. 26CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 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
  27. 27. 27CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 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, …
  28. 28. 28CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ The ML sequence model approach to NER Training 1. Collect a set of representative training documents 2. Label each token for its entity class or other (NA) 3. Design feature extractors appropriate to the text and classes 4. Train a sequence classifier to predict the labels from the data Testing 1. Receive a set of testing documents 2. Run sequence model inference to label each token 3. Appropriately output the recognized entities
  29. 29. 29CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 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).
  30. 30. 30CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ 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
  31. 31. 31CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Named Entity Recognition using multi-layered bidirectional LSTMs (Cont.) Tokenizing Stemming Word2vec/Glove Training (BiLSTM) Test and get accuracy 64gb microsdxc card class 10Word2ve c model Bi- LSTM Softmax Word vectors Embeddin g For words capacity type type transmission transmission speed speed Bi- LSTM
  32. 32. 32CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ Examples and Detection Accuracy Ø Sample text inputs and the classification results for SAP Catalogs. hd capacity: 2.5tb hdd 32gb ssd memory (ram): 6gb ddr3 operational system: windows 7 type: c560 ultraslim notebook processor: 3.5 ghz intel core hd capacity: 256gb memory (ram): 8gb 2133mhz lpddr3 sdram color: space gray video card: iris plus graphics 640 type: macbook pro processor: 2.3ghz dual core intel core i5 turbo boost up to 3.6ghz intel 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 Positive Negative True 344 63 False 35 276 Results (accuracy: 86%)
  33. 33. Questions?
  34. 34. SAP Leonardo Machine Learning Thank you

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See how SAP Leonardo is enabling the development of more intelligent enterprises with the power of machine learning, big data, and NVIDIA GPUs.

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