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Think Big | Enterprise Artificial Intelligence

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Resurgence of AI and use cases in the enterprise. Think Big, a Teradata company.

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Think Big | Enterprise Artificial Intelligence

  1. 1. 1 1 Think Big Analytics
  2. 2. 2 2 Think Big Overview Kafka © 2017 Think Big, A Teradata Company 1st Big Data only service provider • Founded in 2010 - industry thought leader • Technology agnostic with open source integration expertise • Full spectrum consulting, data engineering, data sciences & support • 150+ successful projects & 100+ clients • Global delivery model to balance needs (on-site, near-shore, off-shore) 300 1000 global professionals • Fixed fee option experience for predictable risk and spend
  3. 3. 3 3 Think Big Velocity Services Portfolio Think Big, Start Smart, Scale Fast Training & Mentoring Architecture & Roadmap Data Lake Analytics OpsData Science Managed Services RACE © 2017 Think Big, A Teradata Company
  4. 4. 4 © 2017 Think Big, a Teradata Company 4 Foundation for Enterprise Analytics Artificial Intelligence Analytics Ops Industrial Data Management
  5. 5. 5 © 2017 Think Big, a Teradata Company 5 Join us Work with us!!! https://www.thinkbiganalytics.com/big-data-careers/
  6. 6. © 2017 Think Big, a Teradata Company 6 Think Big — Start Smart — Scale Fast
  7. 7. 7 © 2015 Teradata Applications and Approaches © 2017 Teradata ​Enterprise Artificial Intelligence Laura Frølich Data Scientist
  8. 8. 8 Analytics Evolution Descriptive Predictive Prescriptive What is happening?REPORTING ANALYZING PREDICTING MACHINE LEARNING ARTIFICIAL INTELLIGENCE Is it real? Why is it happening? What are the hidden patterns? What will happen next? Self-learning systems with linear regression. Deep learning. OPERATIONALIZING What is happening right now? ACTIVATING Make it happen with automation. 2010s 2000s 1990s
  9. 9. 9 The Resurgence of Artificial Intelligence • Significant advances in hardware capability • Rapid progress in research and applications using neural networks • Significant technology investments • Increasing amounts of data By 2019, deep learning will provide best- in-class performance for demand, fraud, and failure prediction. - Gartner
  10. 10. 10 Companies mentioning “Artificial Intelligence” Rising Rapidly The Resurgence of AI
  11. 11. 11 “ By 2020 AI will be a top five investment priority for more than 30% of CIOs. —Gartner BI Summit, February, 2017 “The Resurgence of AI
  12. 12. 12 The Resurgence of AI 7 November 2016 12 October 2014
  13. 13. 13 Deep Learning How is it different? • Multiple layers in neural network with intermediate data representations to facilitate dimensional reduction. • Interpret non-linear relationships in the data. • Derive patterns from data with very high dimensionality. Why do we care? • Ability to create value with little or no domain knowledge required. • Ability to incorporate data from across multiple, seemingly unrelated sources. • Ability to tolerate very noisy data.
  14. 14. 14 Deep Learning Innovation in Computer Vision Continuous Improvement in Supervised Learning Methods 2016 Image-Net Results
  15. 15. 15 • Context • Applications • Conclusions Agenda
  16. 16. 16 • Good fit for AI – Massive data amounts – Complex patterns • Bad fit for AI – Small data amount – Limited time for training – Interpretability required • Caveats – Amplification of existing human biases – Blind spots/adversarial challenges - Not unique to deep learning though AI in applications Intriguing properties of neural networks, 2014, Szegedy et al.
  17. 17. 17 • Many of these use cases already have working solutions using non-DL Machine Learning Techniques • Deep Learning is delivering improvement in performance on complex problems Source: http://deeplearning4j.org/use_cases AI Has Many Applications Across Industries
  18. 18. 18 Mobile Personalization • Google Play Store production and other leading digital companies – Generalize rules (e.g., categories of interest) – Memorize exceptions (e.g., common pairs) • Projects in banking, telco, retail Source: Google
  19. 19. 19 Banking Anti-Fraud: Business Drivers • Goal: fraud detection across products • Trends – Evolution of new payment methods – Mobile payments exploding – Fraud evolving rapidly, increased sophistication • Traditional approach is hand-written rules • Cost, delay and customer impact of false positives
  20. 20. 20 • Phased implementation approach – Simulated result – Champion/challenger testing – Production deployment • Significant improvements over traditional rules-based techniques • Techniques – Random Forest – Recurrent Neural Networks • Tools: Spark, Hadoop, TensorFlow Banking Anti-Fraud: Solution Approach
  21. 21. 21 • Provide smart assistance to drivers – Navigation and safety – Realtime Pricing – Vehicle comfort – Parking assistance • Leverage video and other sensors • Techniques: – Object Detection, Segmentation, Motion Detection, etc. – Scene Labeling: Convolutional Neural Network, MultiNet • Tools: TensorFlow, Darknet Connected Car Assistance Real-Time Streaming Streaming Results Traffic Data Service Navigation Update Object Detection Object Segmentation Motion Detection GPU Training TF Serving Online Inference Model Update s
  22. 22. 22 • Handwritten check volume is decreasing however processing checks has many fixed costs • Handwriting recognition to reduce manual processing and fraud examination resulting in cost savings • Techniques: – Convolutional Neural Network – Image Processing – Natural Language Processing • Tools: Spark, Hadoop, TensorFlow Automated Check Processing Check Images To Hadoop ImageMagick Processing Handwriting Recognition
  23. 23. 23 • Market Context • Applications • Conclusions Agenda
  24. 24. 24 Challenges • Technology – Research-driven, rapid change – GPU deployment and integration – Framework immaturity – Research quality model code – Complexity • Point solutions rarely meet bar for enterprise • Limited access to talent • Data – Governance and quality – Volume, kinds – Labeling / supervision • Deployment and integration
  25. 25. 25 Focus First on Pilot into Production Sets up Phase Two: Scale COE, Standardize Capabilities Investigate Test Engineer SimulateIntegration Analyze Data Go Live Handover Validate Activities: Define business opportunity, understand data available, test model approaches, potentially generate data Outcome: Proposed solution approach Discovery/Insights Activities: Architecture selection, software engineering of model and simulation Outcome: Predicted impact of model Live Test Activities: Integration into live business process (Champion/Challenger), analysis, iteration Outcome: Benefit measurement, live learnings, improvement Production Activities: Go Live, Analytics Ops integration, Hand Over Outcome: System scaled, application teams and ops trained and operating Assessment Insights Production Live Test Cross-Functional Teams Cross-Functional Teams
  26. 26. 26 Analytics Ops for Cross-Functional AI Teams Constant Monitoring Test and Deploy- ment A/B Testing Automated Training & Scoring Application Integration
  27. 27. 27 Our Approach Teradata Deep Learning CommunityTeradata Labs Dozens of Experts in Deep Learning, Image/Audio/Video Processing, Computer Vision, GPU 200+ Practitioners delivering Artificial Intelligence Business Value on Customer Projects 500+ Solution Architects, Business Consultants and Software Engineers with knowledge of Artificial Intelligence Tools, Techniques and Technologies. Deep expertise in retail and across industries. Experts Practitioners Interest Industry Collaborations Academic Collaborations Analytics Ops Data Management
  28. 28. 28 Industry Timeline Projection
  29. 29. 29 Conclusion • AI moving beyond labs to production • A strategy and roadmap is critical • Pilot now, build capabilities

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