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Accelerating AI Adoption with Partners

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This session was held by Vladimir Brenner, Partner Account Manager, Disruptors & AI, Intel AI at the Dive into H2O: London training on June 17, 2019.

Please find the recording here: https://youtu.be/60o3eyG5OLM

Publicado en: Tecnología
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Accelerating AI Adoption with Partners

  1. 1. Vladimir Brenner Intel Corporation vladimir.brenner@intel.com Acceleratingaiadoptionwithpartners
  2. 2. 2 Business Imperatives AI with Intel and Partners agenda … on AI/ML/DL
  3. 3. 3 Business Imperative
  4. 4. 4 4TBAutonomousvehicle 5TBCONNECTEDAIRPLANE 1PBSmartFactory 1.5GBAverage internetuser 750pBCloudvideoProvider DailyBy2020 Source: Amalgamation of analyst data and Intel analysis. Business Insights Operational Insights Security Insights Thedelugeofdata
  5. 5. 5 46%of Chief Information Officers (CIOs) have developed plans to implement AI, but only 4% have implemented AI so far. According to a recent Gartner survey… Aiadoptionisnascent Source: Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence. February 2018 (https://www.gartner.com/newsroom/id/3856163)
  6. 6. 6 AI with Intel and Partners
  7. 7. 7 hardware Multi-purpose to purpose-built AI compute from cloud to device solutions Partner ecosystem to facilitate AI in finance, health, retail, industrial & more tools Software to accelerate development and deployment of real solutions BringYourAIVisiontoLifeUsingOurcomprehensive Portfolio
  8. 8. 8 1GNA=Gaussian Neural Accelerator All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice. Images are examples of intended applications but not an exhaustive list. IOT SENSORS (Security, home, retail, industrial…) Display, Video, AR/VR, Gestures, Speech DESKTOP & MOBILITY Vision & Inference Speech SELF-DRIVING VEHICLES Autonomous Driving SERVERS, APPLIANCES & GATEWAYS Latency- Bound Inference Basic Inference, Media & Vision Most Use Cases SERVERS & APPLIANCES DatacenterEdgeEndpoint Flexible & Memory Bandwidth-Bound Use Cases Onesizedoesnotfitall HARDWARE Multi-purpose to purpose-built AI compute from device to cloud Varies to <1ms <5ms <10-40ms ~100ms Dedicated Media & Vision Inference Most Use Cases Most Intensive Use Cases NNP-L M.2 CardSOC Special Purpose Special Purpose
  9. 9. 9 LeadingAIresearch Choose a partner on the cutting-edge of AI breakthroughs Neuromorphic Computing Test Chip Codenamed “Loihi” Quantum Computing 49-Qubit Test Chip Codenamed “Tangle-Lake” NEW AI Technologies @Intel Labs All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice.
  10. 10. 10 † Formerly the Intel® Computer Vision SDK *Other names and brands may be claimed as the property of others. Developer personas show above represent the primary user base for each row, but are not mutually-exclusive All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice. TOOLS Software to accelerate development and deployment of real solutions TOOLKITS App Developers libraries Data Scientists foundation Library Developers * * * * FOR DEEP LEARNING DEPLOYMENT OpenVINO™ † Intel® Movidius™ SDK Open Visual Inference & Neural Network Optimization toolkit for inference deployment on CPU/GPU/FPGA for TF, Caffe* & MXNet* Optimized inference deployment on Intel VPUs for TensorFlow* & Caffe* DEEP LEARNING FRAMEWORKS Now optimized for CPU Optimizations in progress TensorFlow* MXNet* Caffe* BigDL/Spark* Caffe2* PyTorch* PaddlePaddle* DEEP LEARNING Intel® Deep Learning Studio‡ Open-source tool to compress deep learning development cycle MACHINE LEARNING LIBS Python R Distributed •Scikit- learn •Pandas •NumPy •Cart •Random Forest •e1071 •MlLib (on Spark) •Mahout ANALYTICS, MACHINE & DEEP LEARNING PRIMITIVES Python DAAL MKL-DNN clDNN Intel distribution optimized for machine learning Intel® Data Analytics Acceleration Library (incl machine learning) Open-source deep neural network functions for CPU / integrated graphics DEEP LEARNING GRAPH COMPILER Intel® nGraph™ Compiler (Alpha) Open-sourced compiler for deep learning model computations optimized for multiple devices (CPU, GPU, NNP) from multiple frameworks (TF, MXNet, ONNX) * * * ** * * *
  11. 11. 11 *Other names and brands may be claimed as the property of others. All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice. solutions IntelAI Builders builders.intel.com/ai/membership Your one-stop-shop to find systems, software and solutions using Intel® AI technologies Reference solutions builders.intel.com/ai/solutionslibrary Get a head start using the many case studies, solution briefs and more reference collaterals spanning multiple applications Partner ecosystem to facilitate AI in finance, health, retail, industrial & more
  12. 12. 12 INFERENCE THROUGHPUT Up to 277x 1 Intel® Xeon® Platinum 8180 Processor higher Intel optimized Caffe GoogleNet v1 with Intel® MKL inference throughput compared to Intel® Xeon® Processor E5-2699 v3 with BVLC-Caffe 1 The benchmark results may need to be revised as additional testing is conducted. The results depend on the specific platform configurations and workloads utilized in the testing, and may not be applicable to any particular user's components, computer system or workloads. The results are not necessarily representative of other benchmarks and other benchmark results may show greater or lesser impact from mitigations. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit: http://www.intel.com/performance.Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit: http://www.intel.com/performance Source: Intel measured as of June 2018. Configurations: See slide 4. TRAINING THROUGHPUT Up to 241x 1 Intel® Xeon® Platinum 8180 Processor higher Intel Optimized Caffe AlexNet with Intel® MKL training throughput compared to Intel® Xeon® Processor E5-2699 v3 with BVLC-Caffe Deliver significant AI performance with hardware and software optimizations on Intel® Xeon® Scalable Family Optimized Frameworks Optimized Intel® MKL Libraries Inference and training throughput uses FP32 instructions Intel®Xeon®processors Now Optimized For Deep Learning
  13. 13. 13 … on AI/ML/DL
  14. 14. 14 1
  15. 15. 15 automatingaitransformation…together • Extensive Tech IP Leadership • Comprehensive Partner Ecosystem • End to End Analytics and AI Solutions • Customer Value Driven • Open Source AI Leadership • AI Democratization Commitment • Tremendous AI momentum • Customer Value Driven Project“bluedanube” • Increase AI adoption in the Enterprise • Enable H2O.ai technologies on Intel AI platforms • Optimize H2O.ai Machine Learning algorithms, libraries and models on Intel Architecture • Advance Data Science education through H2O.ai Academy • Accelerate H2O.ai objective of “using AI to optimize AI” + “Shared vision for accelerating and Democratizing AI in the enterprise” – Sri Ambati, H2O.ai CEO & Founder
  16. 16. 16 Intelandh2o.aiIntegratedAISolutions Source: Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence. February 2018 (https://www.gartner.com/newsroom/id/3856163) Intel® 3D NAND SSD H2O Driverless AI Intel® Xeon® Scalable Processors Intel® Ethernet Adaptors Intel® Data Analytics Acceleration Library (Intel® DAAL) Open Visual Inference and Neural Network Optimization (OpenVINO™ toolkit) Intel® Optimized Deep Learning Frameworks Intel® Math Kernel Library (Intel® MLK) H2O Open Source Platform H2O Sparkling Water Intel Platforms Intel Optimized Libraries and Frameworks H20.ai Machine Learning Platforms Business Verticals & Performance Efficient Customer Use Cases
  17. 17. 17 Calltoaction Source: Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence. February 2018 (https://www.gartner.com/newsroom/id/3856163) Work with & for your AI / ML / DL Related Projects Visit: h2o.ai - software.intel.com – 01.org
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  19. 19. 19 This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the OEM or retailer. No computer system can be absolutely secure. Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult other sources of information to evaluate performance as you consider your purchase. For more complete information about performance and benchmark results, visit http://www.intel.com/performance. Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and provide cost savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction. Statements in this document that refer to Intel’s plans and expectations for the quarter, the year, and the future, are forward-looking statements that involve a number of risks and uncertainties. A detailed discussion of the factors that could affect Intel’s results and plans is included in Intel’s SEC filings, including the annual report on Form 10-K. The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether referenced data are accurate. Intel, the Intel logo, Pentium, Celeron, Atom, Core, Xeon, Movidius and others are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © 2018 Intel Corporation. Legalnotices&disclaimers

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