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Dell NVIDIA AI Powered Transformation in Financial Services Webinar

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Dell NVIDIA AI Powered Transformation in Financial Services Webinar

  1. 1. Digital Transformation Through Data Analytics AI Powered Transformation Financial Services Dell / NVIDIA AI Roadshow Bill Wong – Dell Technologies Artificial Intelligence and Data Analytics Practice Leader Adam Shubinsky – NVIDIA Data Science & High Performance Compute Solutions Leader Dorin Nistor – Dell Technologies Financial Services Solution Consulting Senior Manager
  2. 2. Agenda  Key Business Challenges and Trends  NVIDIA Update  AI Transformation Challenges  NVIDIA Infrastructure Solutions  Dell Technologies AI Strategy  Industry Trends  AI Partner Ecosystem  Summary  Dell NVIDIA Partnership
  3. 3. COVID-19 and the New Reality Business Challenges  Increased reserves for defaults and credit risk  Increased customer call volumes Technology Challenges  Increased security risks - Phishing and malware delivery schemes  Increased network and bandwidth volumes Labour Challenges  Collaborating more with remote teams
  4. 4. Today’s Business Trends Trend #1: Remote working will be a prevalent way of working Trend #2: Organizations across industries will increasingly rely on digital platforms/channels to increase future resilience and growth Trend #3: Data and analytics become essential in assisting faster and better decision-making According to IDC, By 2025, at least 90% of new enterprise apps will embed artificial intelligence. - Most of these will be AI-enabled apps, delivering incremental improvements to make applications "smarter" and more dynamic.
  5. 5. THE MARKET FORCES SHAPING COMPUTING Breakdown of Dennard Scaling Amdahl’s Law End of the Line 1,000X by 2025 1,000X by 2025 1980 1990 2000 2010 2020 102102 103103 104104 105105 106106 107107 Single-threaded perfSingle-threaded perf 1.1X per year1.1X per year GPU-Computing perf 1.5X per year 1.5X per year1.5X per year ARTIFICIAL INTELLIGENCE SCIENTIFIC COMPUTING DATA ANALYTICS Sources: A New Golden Age for Computer Architecture, by John L. Hennessy, David A. Patterson Communications of the ACM, February 2019, Vol. 62 No. 2, Pages 48-60. 10.1145/3282307 https://cacm.acm.org/magazines/2019/2/234352-a-new-golden-age-for-computer-architecture/fulltext Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp
  6. 6. OVERCOMING DATA PROCESSING CHALLENGES Meeting Data-Processing requirements, Today and Tomorrow 2030202020102000 Data Processing Requirements CPU Data to Analyze GPU 3.03.0 Hadoop Era Spark Era Spark + GPU Era Learn More - nvidia.com/spark-book “These contributions lead to faster data pipelines, model training and scoring for more breakthroughs and insights with Apache Spark 3.0 and Databricks.” — Matei Zaharia, original creator of Apache Spark and chief technologist at Databricks Spark 3.0 scale-out clusters are accelerated using RAPIDS software and NVIDIA GPUs GPU year-over-year performance increases to meet and exceed data processing requirements
  7. 7. AI WORKLOADS: FROM TRAINING TO INFERENCE Untrained Neural Network Model Deep Learning Framework TRAINING Learning a new capability from existing data Trained Model New Capability App or Service Featuring Capability INFERENCE Applying this capability to new data Trained Model Optimized for Performance
  8. 8. THE KEY CHALLENGE: ACCELERATING BIG & SMALL AI Advances Demand Exponentially Higher Compute AI Applications Demand Distributed Pervasive Acceleration 3000X Higher Compute Required to Train Largest Models Since Volta Every AI Powered Interaction Needs Varying Amount of Compute AlexNet ResNet BERT GPT-2 Megatron-GPT2 Turing NLG Megatron-BERT 1E-03 1E-02 1E-01 1E+00 1E+01 1E+02 1E+03 2012 2013 2014 2015 20162017 2018 2019 20202021 Petaflop/s-Days 3000X AI Interactions Per Day Source: OpenAI, NVIDIA
  9. 9. TODAY’s HYPERCONVERGED DATA CENTER Impossible to Optimally Design Server Mix for Unpredictable Demand
  10. 10. SOLVING THE INFLEXIBILITY OF AI INFRASTRUCUTURE Not Optimized, Complex to Manage, Difficult to Scale Predictably Inflexible infrastructure that was never meant for the pace of AI Constrained workload placement by system-level characteristics Non-uniform performance across the data center Unable to adapt to dynamic workload demands Constrained capacity planning TRAINING CLUSTER ANALYTICS CLUSTER INFERENCE CLUSTER
  11. 11. CONSOLIDATING DIFFERENT WORKLOADS ON DGX a100 One Platform for Training, Inference and Data Analytics TRT TRT TRT TRT TRT TRT TRT TRT TRT TRT TRT TRTT TRT TRT Instance 1 Instance 7 Instance 14Instance 8 2x A100s for inference in MIG mode Data Analytics Training 4x A100s 2x A100s
  12. 12. 12 ELASTIC AI INFRASTRUCTURE WITH DGX A100 DGX A100 with MIG Delivers New Agility for Today’s Enterprise Data Center DGX A100 Infrastructure is Agile DGX A100 infrastructure uses MIG to allocate GPU resources to workloads TRAINING CLUSTER ANALYTICS CLUSTER INFERENCE CLUSTER OVEROPTIMAL UNDER Infrastructure silos starve AI workloads or waste capacity ANALYTICS INFERENCE TRAINING Toda y Tomorro w Next Week Traditional Infrastructure is Constrained
  13. 13. 13 DGX A100 LOWERS TCO WITH MAXIMIZED UTILIZATION Legacy infrastructure is inflexible Sits idle when demand drops, unable to scale when demand increases Nearly impossible to optimize utilization Adapt to Changing Business Needs Without Reinvesting BEFORE AFTER DGX A100 is agile, outperforming legacy for every AI workload: analytics, training, and inference Adapts to business demand providing a single elastic infrastructure that’s more efficient Better utilization = lower TCO and faster ROI on AI 0 10 20 30 40 50 60 70 80 90 100 Training Cluster Time 0 10 20 30 40 50 60 70 80 90 100 Combined Workloads on… Time Target utilizationTarget utilization
  14. 14. 14 TODAY’S AI DATA CENTER 50 DGX-1 systems for AI training 600 CPU systems for AI inference $11M 25 racks 630 kW
  15. 15. 15 5 DGX A100 systems for AI training and inference $1M 1 rack 28 kW 1/10th COST 1/20th POWER $1M 28 kW DGX A100 DATA CENTER
  16. 16. THE NVIDIA POWERED INFRASTRUCTURE Reducing costs, power-consumption, and server footprint 1/5th THE COST 1/5th THE COST 1/3rd THE POWER 1/3rd THE POWER 1/5th THE COST 1/3rd THE POWER $10M 140 kW$10M$10M 140 kW140 kW 163 GB/s Throughput on RAPIDS Implementation of TPCx-BB @ SF 10K $2M | 16 DGX-1 | 2 Racks | 56 kW Learn More - nvidia.com/spark-book Equivalent 163 GB/s Throughput on TPCx-BB @ SF 10K $10M | 167 2U CPU Systems | 11 Racks | 140 kW
  17. 17. 25 Years of Accelerating Computing The NVIDIA philosophy: One systems architecture — many uses X-FACTOR SPEED UP FULL STACK DATA-CENTER SCALE GPU CPU DPU ONE ARCHITECTURE
  18. 18. Identify the Game Changer Technologies for Your Organization Nearly half of all respondents (150 financial services/fintech executives surveyed) see a major competitive threat in “Big Tech” firms leveraging AI capabilities to enter financial services. World Economic Forum Global AI Survey, 2020
  19. 19. AI/ML/DL is the fastest growing Datacenter workload Worldwide AI Spending ~$98 Billion by 2023 Overall CAGR = 28.5% • H/W CAGR=24.1% • S/W CAGR=36.7% • Services CAGR=25.9%
  20. 20. AI in Financial Services Customer Experience Chatbots can use advanced image recognition and social data to personalize sales conversation Customer Acquisition Classify customer wallets into micro- segments to establish finely-tuned marketing campaigns and provide AI-driven insights on the next best offers Customer Support Analyze voice biometrics in phone conversations, to provide real-time guidance, help agents tailor their speaking style, and send instant feedback about agent call performance and customer perception. Data-Driven Cybersecurity Analyze data such as IP addresses, geographic data, email domains, mobile device types, operating systems, browser agents, phone prefixes, and more to prevent or remediate account takeovers Fraud Mitigation Real-time analysis to identify and detect and prevent fraud in all avenues of commerce including online and in-person transactions IoT / Sensor-based Analytics Collect and analyze IOT and usage-based source data vs. proxy data to determine risk premiums Industry Application Examples Alpha Stock Identification Analyze Consumers’ Behavior Anti-fraud API Service Insurance Campaign And Conversion Analysis Credit Card Application Approval Customer service chatbots/routing Customer service chatbots/routing Claim Fraud Detection Evaluate Create Worthiness Fraud And Credit Risk Analysis Fraud Detection Hedge Fund Management Risk evaluation and more… The Next Normal for Engaging Customers
  21. 21. Financial Services Data Lake Supporting Digital Transformation through Advanced Analytics Consumption Zone / Data Analytics Raw / Landing/ Secure Zone/ Data Ingestion Documents and Emails Web logs, Click Streams, Newsfeeds, IOT/Sensor data Self-Service Dashboards Advanced Analytics Sales Analysts Consumer Dashboards Operational Analytics Data Scientists Customers Marketing Analysts Data Governance | Security and Compliance Enriched / Discovery Zone / Data Transformation Data Sources Common Services Optimized Infrastructure for Advanced Analytics Chat data Personas Tools / Applications Data Lake Capabilities • Provide support for a variety of analytical applications, including self-service, operational, and data science analytics • Data preparation and integration capabilities to ingest structured and unstructured data, move and transform raw data to enriched data, and enable data access to for the target user base • An infrastructure platform optimized for advanced analytics that can perform and scale OLTP, ERP, CRP Data Social Networks Machine Generated Data
  22. 22. Expectations Plateau of Productivity Peak of Slope of EnlightenmentInnovationTrigger Trough of Disillusionment Inflated Expectations Hype Cycle for Artificial Intelligence “Narrow" AI is becoming better than humans at defined tasks. "General" AI is still a long way off.” Time Plateau will be reached less than 2 years 2 to 5 years 5 to 10 years more than 10 years Deep Learning Infrastructure Transformation Autonomous Vehicles “AI, one of the most disruptive classes of technologies, will become more widely available due to cloud computing, open source and the “maker” (developers, data scientists and AI architects) community. While early adopters will benefit from continued evolution of the technology, the notable change will be its availability to the masses. As of July 2019 AI PaaS Artificial General Intelligence Machine Learning NLP FPGA Accelerators GPU Accelerators DNN ASICs Quantum Computing Neuromorphic Hardware Computer Vision Speech Recognition
  23. 23. Top 10 Types of Hardware for AI Delivery* 1. Processors (CPU, GPU, FPGA, ASIC) 2. HPC / Supercomputer Infrastructure 3. Communication Network 4. Personal Devices 5. Connected Home Devices 6. AR / VR Head-Mounted Displays (HMD) 7. Drones 8. Robotics 9. Automotive 10.Sensors and Application Components (audio, camera, LiDAR, etc.) *The Business Impact and Use Cases for Artificial Intelligence, Gartner, 2017 Accelerate computational performance AI-enabled endpoints AI-enabled autonomous endpoints
  24. 24. MasterCard - Protecting what’s in your wallet Business need Turning 2.2B global cards 160M transactions/hour, 52B transactions/year into intelligence with 1.9M rules applied to those transactions. Benefits • Understand how, when and where customers buy leading to predictive analytics • Increased security and fraud protection via analyzing customer purchasing patterns, affinities and rhythms using machine learning • Expand business with anonymized data re: share of wallet compared to competition, average ticket and frequency to identify marketing opportunities and measure return on investment Solution Mastercard worked with Dell EMC and Cloudera to build a secure, PCI-certified Hadoop cluster. Mastercard's Enterprise Data Hub fully conforms to the PCI-DSS V 2.0 security standards so it can host PCI datasets and integrate with other systems. “Data privacy and protection is a top priority for Mastercard. As we maximize the most advanced technologies from partners and vendors, they must meet the rigorous security standards we’ve set.” Gary VonderHaar, Chief Technology Officer, Architecture, MasterCard https://www.youtube.com/watch?v=uGoQ_6-E_sQ
  25. 25. FASTER PROCESSING, HAPPIER CUSTOMERS The insurance industry still relies largely on evidence-based, non-standardized documents such as paper, scans, and photos for contract management. Processing this type of documentation is often manual, tedious, and time consuming for the insurer and the insured. ‘Cardif Forward’ is BNP Paribas Cardif’s innovative digitization plan and AI is a key element. The company—known for leading-edge customer service—is developing GPU-accelerated deep learning image recognition algorithms to automatically recognize and process documents digitized by its clients. The AI solution will significantly reduce the complexity of contract management and speed the process.
  26. 26. AI-DRIVEN ASSET MANAGEMENT AI has led to break-through innovations across all industries and the finance industry is no exception. qplum, an online asset management firm, uses quantitative trading techniques and invests using data and GPU-powered deep learning. qplum blends the mathematics of data-driven decision-making, the science of behavioral economics, and the art of effective communications. In the speed trade category, qplum has been an innovation leader having started with a $10,000 risk limit and, over the last 10 years, making more than $1.4B in profits.
  27. 27. REAL-TIME FRAUD DETECTION Recently, PayPal was looking to deploy a new fraud detection system. The team working on it set a high bar: this system had to operate worldwide 24/7, and work in real-time to protect customer transactions from potential fraud. In spec’ing the system, it became evident that CPU-only servers couldn’t meet these requirements. Using NVIDIA T4 GPUs, PayPal delivered a new level of service, using GPU inference to improve real-time fraud detection by 10% while lowering server capacity by nearly 8x.
  28. 28. 6000x Speedup on Key Trading Algorithm Backtesting is a way to assess the viability of a trading strategy. It’s a method of testing a trading model with historical data to see how it would perform under real-world circumstances.  20 million trading simulations can now be completed in one hour. That’s 6,000X faster than the previously set benchmark of 3,200 simulations in one hour.  NVIDIA DGX-2 and accelerated Python libraries provide unprecedented speedup for STAC-A3 algorithm used to benchmark backtesting of trading strategies.  Data scientists and traders can replicate this performance without needing in-depth knowledge of GPU programming with RAPIDS and Numba software. 3,200 20,000, 000 0 5,000,000 10,000,000 15,000,000 20,000,000 20x Cloud Nodes DGX-2 Simulations / Hour Simulations / Hour is STAC-A3.β1.SWEEP.MAX60 DGX-2 is STAC SUT ID NVDA190425 20x Cloud Nodes is STAC SUT ID HPAT171029 Link to blog: https://blogs.nvidia.com/blog/2019/05/13/accelerated-backtesting-hedge-funds/ Link to STAC Release: www.STACresearch.com/news/2019/05/13/NVDA190425 NVIDIA DGX-2 WITH ACCELERATED PYTHON DELIVERS FASTER PROCESSING MORE SIMULATIONS MORE ACCURATE ALGORITHMS
  29. 29. AI Accelerators Flexibility Efficiency and many more…
  30. 30. Deep Learning Analytics – GPU, Graphcore Dell Technologies – AI Compute Platforms Performance Inference Data Analytics Multi-App HPC / ML / DL C6420pC6420p R840 DS8440 8+ 4 2 - 3 1 Solution price $ C4140C4140 GPU DB Acceleration, AI/ML R940xa SDS/VDI R740XDR740XD 1:1 CPU/GPU ratio Highest density of CPU and memory with 2 GPUs GRAPHCORE IPU XILINUX FPGA INTEL FPGA NVIDIA GPU INTEL CPU AMD CPU GRAPHCORE IPU XILINUX FPGA INTEL FPGA NVIDIA GPU INTEL CPU AMD CPU
  32. 32. Combining AI with HPC to Improve Predictions AI Predictive Trading Gauge the market attitude or generic emotions towards a particular security, using the news reports, blog posts, twitter messages etc. Sentiment analysis algorithm is then used to infer whether the market sentiment is bullish or bearish Sentiment Analysis HPC Analytics Increase Prediction Accuracy A Monte Carlo Tree Search algorithm can be used to generate additional data, which can then be fed into a neural network for training. Resulting in a larger training set of data for the AI model
  33. 33. Dell EMC Data Science Platform Nauta ClaraAI KubeFlow NVIDIA EGX Domino Cassandra HPCaaS Metropolis Spark Jupyter Bright Cluster Manger Dell-curated Ansible/ Terraform playbooks CNI MetalLB CoreDNS Prometheus NFS provisioner Helm Kubernetes Linux (RHEL/CentOS) + CRI (Docker/containers) 1 https://infohub.delltechnologies.com/section-assets/h18136-tco-analysis-dell-emc-hpc-ra-for-ai-da-sb On-premises system for HPC, AI and Data Analytics AI / Machine learning / Deep learning PowerSwitch S3148-ON S5232F-ON cluster switch PowerEdge R740 management and compute nodes PowerEdge C4140 acceleration nodes DSS 8440 dense acceleration nodes Dell EMC Isilon Dell EMC Ready Solution for HPC BeeGFS Storage Dell EMC Ready Solution for HPC NFS Storage © Copyright 2020 Dell Inc. HPC AI Ready Architecture One Platform for AI, Data Analytics, and Simulation Workloads • Simplified operations and lower cost while enabling new use cases for users at the lowest TCO1 • Allow HPC, DA & AI workloads to execute on the same cluster; reducing data movements for faster results • Run simulation & modeling, analytics, visualization, and AI workloads on a common HPC infrastructure Software ecosystem
  34. 34. AI Magic Quadrants Data science and machine-learning platforms are defined as: • A cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products. Cloud AI developer services are defined as: • Cloud-hosted services/models that allow development teams to leverage AI models via APIs without requiring deep data science expertise Data Science and Machine Learning Platforms Cloud AI Developer Services The Marketplace Continues To Evolve
  35. 35. Data Analytics and AI Use Cases – Partner Solutions IOT / Streaming / Machine Data Analytics Deliver Near Real-Time Analytics • Analyze IOT / Streaming data • Improve IT operations and security leveraging Machine Data • Computer vision applications Machine / Deep Learning Transform the business with analytical insights • Data Science / Machine Learning Platform • Industry-focused AI platforms Data Lake/Unstructured Data Infrastructure Improving Data Access and Agility • Create an enterprise data platform for structured and unstructured data • ETL offload to lower costs • On-demand deployment of container-based environments Augmented Analytics and Data Warehouse Improve Decision Making • Support augmented business analytics • Create an enterprise data platform to support analytics • Data integration and Master Data Management *Note, some products can deliver capabilities that address multiple use cases
  36. 36. AutoML offerings enables business users and the Citizen Data Scientist • Easy to use, you do not need to be a data scientist • Prediction Explanation: Highlights the features that impact each model’s decision H2O.ai DataRobot AutoML offerings H2O Driverless AI (commercial) and H2O-3 (open source) • Good adoption of its open source offering • Machine Learning Interpretability generates the constructs for the data scientist to use and explain the results of the models
  37. 37. Accelerate Time From Research To Production With An AI ML Platform • Micro-services based and full stack data science platform. Decouple infrastructure from the data pipeline microservices. A code-first platform ready to integrate any containerized tools and open source • Accelerate AI development with reusable ML components, and production-ready infrastructure with native Kubernetes cluster orchestration and meta-scheduler. Iguazio Open and High Performance Data Science PaaS • Managed & hardened open-source plus 3rd party services and apps • Secure real-time data sharing enabling collaboration & parallelism • Minimize CPU, mem, and ops overhead Cnvrg.io
  38. 38. Customer and Employee Health and Safety Solutions • Detection of persons/objects • Display showing temperature differences accurate to 0.1°C • Alarm in case of exceeding or falling below defined temperature ranges • Event Triggers (alarm, network message, activation of a switching output) • Temperature range from -40 to +550 °C •Face Redaction for privacy Dell Workstation with NVIDIA Dell Technologies Surveillance Solutions - Open Data Lake Platform - Scalable Infrastructure - Analytics-ready Image, Video and Thermal-based AI Applications Applications - Fraud Detection - Loss Prevention - Workplace Accident Reduction - Customer Insight - Public Safety - Counter Terrorism
  39. 39. • Eliminate inefficient islands of storage – Infrastructure consolidation for both clinical and non-clinical workloads • Scales as data growth and number of instruments, modalities, and digital clinical applications increases • Enable better information sharing • Accelerate data analytics to gain new insight • Extends into the cloud • Prepared for next generation analytics Dell EMC Data Lake Caffe2 Data Lake Storage Platform
  40. 40. The Digital Future Demands a New Perspective Cloud First Data First Infrastructure-centric Business-centric Takes into consideration: • Data gravity • Data velocity • Data control • Data privacy and compliance Driven by: • Lower infrastructure CapEx • Offload infrastructure maintenance • Improve time to market (deployment time for infrastructure) Evolve to a Data-Driven Business
  41. 41. Decision Criteria for AI Infrastructure/Solutions Data Scientist Perspective IDC 2018
  42. 42. • Design and build systems for HPC and Deep Learning workloads • Systems include compute, storage, network, software, services, support • Integration with factory, software, services • Power and performance analysis, tuning, best practices, trade-offs • Focus on application performance • Vertical solutions • Research and proof of concept studies • Publish white papers, blogs, conference papers • Access to the systems in the lab delltechnologies.com/innovationlab Dell Technologies HPC and AI Innovation Lab
  43. 43. The Value of Dell for AI Infrastructure - Comprehensive and Scalable AI/Analytics Platform Portfolio - Workstations, Servers, Clusters, Storage, Networking - Infrastructure and Data Science and Analytics Expertise - HPC and AI Innovation Lab - IoT / Intelligent Video Analytics Lab - Solution-based Offerings - Pre-configured AI Ready Offerings - IoT / Safety and Security and Thermal Vision Solutions - GPU Virtualization - ML Platforms Infrastructure Scalability Reduce Complexity Address Demand Partner Ecosystem Cost Effective
  44. 44. - Appendix - Dell Technologies AI and Data Analytics Solutions
  45. 45. Dell Technologies AI and Data Analytics Solutions AI / Machine Learning / Deep Learning • Domino Data Science Platform Design Document • HPC for AI and Data Analytics Ready Architecture • Retail Loss Prevention Ready Solutions • DataRobot Reference Architecture • H2O AI Reference Architecture • Kubeflow Reference Architecture • OneConvergence Dkube Reference Architecture • Iguazio Reference Architecture • Deep Learning with NVIDIA Ready Solutions • Isilon with NVIDIA DGX-1 Reference Architecture • Isilon with NVIDIA DGX-2 Reference Architecture • Isilon with Dell Precision 7920 Data Science Workstation Reference Architecture • Isilon with Dell EMC DSS8440 Reference Architecture • Noodle.ai (OEM) Solution Bundle IoT / Streaming / Machine Data Analytics • IntelliSite (OEM) Thermal Detection Solution • Retail Loss Prevention Ready Solutions • Dell IoT Safety and Security Portfolio • Real-Time Data Streaming Ready Architecture • Splunk Enterprise on Dell EMC Infrastructure • Streaming Data Platform • ElasticSearch (OEM) Solution Bundle © Copyright 2020 Dell Inc. Augmented Analytics and Data Warehouse • Spark on Kubernetes • Kinetica (OEM) Solution Bundle • ThoughtSpot (OEM) Solution Bundle • Pivotal Greenplum • Dell Boomi Data Lake / Unstructured Data Infrastructure • Microsoft SQL Server 2019: Big Data Cluster Ready Solution • Cloudera Hadoop Ready Architecture • Hortonworks Hadoop Ready Architecture • Kubernetes Containers with Diamanti (OEM) Solution Bundle • Grid Dynamics Reference Architecture • Red Hat OpenShift Reference Architecture HPC Ready Solutions • HPC Digital Manufacturing • HPC Life Sciences • HPC Research • HPC BeeGFS Storage • HPC Lustre Storage • HPC NFS Storage • HPC PixStor Storage *Note, some products can deliver capabilities that address multiple use cases Product Offerings and Technical Collateral for Analytical Use Cases