Uncover the Power of a Big Data Platform Machine Learning at Work

Transcription

1 Uncover the Power of a Big Data Platform Machine Learning at Work Vivek RR / Balasubramanya Nagaraj, SAP July, 2017

2 Agenda SAP Leonardo Machine Learning Foundation SAP HANA External Machine Learning AFL Demo1 Demo2 2

3 SAP Leonardo Machine Learning Foundation Infuse your apps with intelligence Vivek RR / Balasubramanya Nagaraj, SAP July, 2017

4 Digitization Every company to become a software-driven company by Connected Data Integration Collaboration Mobile Big Data Business Process Innovation Intelligently connecting People, Things and Businesses Machine Learning APIs Natural Language Real-time Analytics IoT Networks Microservices 4

5 Machine learning is the reality behind artificial intelligence What is machine learning? Computers learn from data without being explicitly programmed. Machines can see, read, listen, understand, and interact. Why now? 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 5

6 How enterprise data is transformed into business value From data to insights Input Machine learning Output Text Video Train model Applications (such as cash application) Speech Prepare data Apply model Image and more Capture feedback Services (such as invoice processing, profile matching) and more 6

7 SAP s vision for enterprise machine learning SAP Leonardo Machine Learning Create your own intelligent infrastructure Automate Knowledge Work Transformational HR services Lights out finance operations Self-driving customer service Conversational sales bots Customer retention insights Do the Impossible Image-based Ariba commerce Contextual Concur travel concierge Video-aware marketing Visual store execution Drone and satellite-based asset management Vision-enabled manufacturing Contextual logistics based on SAP Cloud Platform and SAP HANA 7

8 Build your intelligent enterprise now SAP Leonardo makes machine learning incredibly simple Assets 76% of the world s transaction revenue touches an SAP system 25 Industries 12 LOB s The world s largest business network SAP Leonardo Machine Learning Applications Business services Functional services Foundation Lifecycle management SAP Cloud Platform & SAP HANA Increase revenue Re-imagine processes Quality time at work Customer satisfaction Enabling innovations 8

9 SAP HANA External Machine Learning AFL Tensor Flow Integration Scenario Vivek RR / Balasubramanya Nagaraj/Jeffrey Lord Jr, SAP July, 2017

10 HANA Machine Learning Capabilities What s Available now? HANA ML capabilities PAL 70+ (and growing) classical ML algorithms APL Domain specific and largely automated ML algorithms Familiar Application development Direct AFL Call Modelled Calc View (Modelling) Transparent invocation of ML capabilities regardless of application language mix ABAP Java Javascript AFL Mechanism Extend HANA capabilities through additional functinality Used for implementing high performance functionality beyond SQL/SQLScript: Linear optimization (OFL) Machine Learning (PAL/APL) Complex Business Calculations (BFL). 10

11 HANA Machine Learning Capabilities : EML AFL What s New (HANA 2.0 SPS02)? Expose additional state of the art ML functionality as part of HANA core Retain the familiar development environment Feels like HANA on the consumption side Feels like XXX on the data scientist side Implemented as service client LAN and WAN capability Efficient, secure communication (grpc) Ensures smooth separable evolution of HANA and the external ML framework is possible Initial Scope Google Tensorflow TensorFlow Serving SAP Leonardo Platform services Standard REST Calls 11

12 Why TensorFlow Open Source library by Google (open sourced just over 1 year ago) Based on data flow graphs C/C++ backend, python API Supports hardware accelerators and distributed processing Extensive built-in support for deep learning Recently released 1.0 Covers both ML research and production Provides a comprehensive framework to evolve a ML model from experimentation to large scale production Source: Jeff Dean Tensorflow Dev Summit

13 and the ML community loves it! Similar to other frameworks like Caffe, Theano, Torch and others.. #1 repository in ML category on GitHub About 1000 commits/month 5500 GitHub repositories related to TF Source: Jeff Dean Tensorflow Dev Summit

14 Objectives of the integration of HANA with Google TensorFlow 1 Extend HANA's capabilities including image processing functionality 2 Provide a seamless (SAP native-like) integration of Google Tensor Flow into HANA for the developer 3 Allow asynchronous queries of multiple Tensor Flow models in parallel to enhance execution speed 4 Enhance HANA business data with features from unstructured image data 14

15 EML (External Machine Learning) AFL Architecture AFL Framework TensorFlow Serving Server R Active Model(s) EML AFL HTTP/2, protobuf Input/Output Table Handling Request handling grpc client REST client R grpc server HTTP/1.1, JSON Model persistence SAP Leonardo Model 1 Model n Machine Learning Platform 15

16 Deployment Options SAP HANA Landscape EML AFL Integrated Indexserver SLES 12 TensorFlow Serving processes Share the HANA cluster resources Simple for test and development SAP HANA Landscape EML AFL Indexserver SLES 12 TensorFlow Landscape TensorFlow Serving arbitrary Dedicated OnPremise Performance isolation Leverage special hardware resources Dynamically scale additional resources SAP HANA Landscape Google Cloud Platform EML AFL Indexserver SLES 12 TensorFlow Cloud Harness the power of dedicated hardware (TPUs) on the Google Cloud Platform or elsewhere 16

17 Demo 1: Goods receipt / Quality inspection Goods received at wholesaler/distributor Quality control (manual) Record results of QA and trigger follow-on activities Ship to retail shops waste/scrap Tomato Lot TM1 TM2 TM3 Quality good rotten good 17

18 Innovation task - apply image processing Your Challenge: Replace the manual entry of quality status with advanced computer vision All other parts of your application expect the simple table below Tomato Lot TM1 TM2 TM3 Quality good rotten good 18

19 Replace manual inspection by image processing SAP HANA Tomato Lot Quality Good score Rotten score TM1 good ,15 TM2 rotten TM3 good 0,

20 Building the demo Data Scientist Create and train a model using transfer learning Package the model for TensorFlow-Serving as a servable Developer/Landscape Admin. Deploy the model to a TensorFlow Serving server resource Register the model with the EML AFL to create a new custom procedure to access the model 20

21 Transfer learning: train custom deep ML nets on a budget Machine learning technique where knowledge gained during training on one type of problem is applied to a similar type of problem. Deep Networks trained on real world objects exhibit a hierarchy of learned representations: Low-level features: dots, lines, curves Middle-level features: simple geometric objects Top-level features: cars, houses, These features are generic and can be applied to other type of problems 21

22 Our training set (examples) Good Rotten 22

23 Our test set Good Rotten Good Good Rotten Rotten 23

24 Demo 2 : The use case of IRIS a fictitious retail company IRIS products are sold in malls individual shops and by external resellers IRIS is an SAP customer on the path of digital transformation Already leverages SAP products HANA, Hybris e-commerce, S/4 HANA Determined to provide the best tailor-made recommendations to its customers Created a mobile app to allow the customer to: Photograph a product of interest Recognize the product Find similar products Finally, to obtain additional recommendations based on the customer's individual transactional history 24

25 The architecture: SAP HANA 2 and Google TensorFlow Acquire Image Identify User Location Analyze Data Identify Brand, Color, and Sleeve Type Recommend Product Generate Recommendations 25

26 Multi-Step Approach All search queries, images and the generated results are stored by the system, allowing a live tracking and prediction of trends in demand and to forward this information to further SAP products e.g. Hybris e-commerce 05 Visualization & Reporting Training data & Training 00 The Inception model from TensorFlow framework is extended with new feature categories (e.g. type of clothing, length of sleeves, color, style, etc.) and trained with example image data in an offline process In addition, store locations close to the customer are searched, which have in stock the products contained in the recommendation results. 04 Geospatial location Image acquisition 01 The image is acquired and send via the phone to an XS Advanced application, which stores it into HANA together with the user geo location Once TensorFlow returns the features, HANA leverages the PAL recommendation engine to search through thousands of records and find similar and associated products to generate a personalized recommendation. 03 Recommend ation engine Product recognition 02 The prepared TensorFlow models are queried by HANA in order to classify and extract the descriptive features of the product clothing type, sleeves length, color, etc. 26

27 High-level architecture Application Server (XS) Mobile/ ios Application Function Library (AFL) Google TensorFlow Service Spatial Engine Spatial Data Spatial Functions Types Geocoding Maps & POI Predictive Analysis Libraries (PAL) Recommendation related data 27

28 Thank you. Contact information: Vivek RR / Balasubramanya Nagaraj v.rr@sap.com / balasubramanya.n@sap.com Global CoE Digital Business Services