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AI meets Big Data
This document discusses artificial intelligence and machine learning. It provides a brief history of AI from the Perceptron model in 1958 to modern deep learning approaches. It then discusses several applications of machine learning like image classification, medical diagnosis, and autonomous vehicles. It also discusses challenges like distributed machine learning and hidden technical debt. Finally, it provides examples of how AI can be applied to commerce and automotive use cases.
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AI meets Big Data
- 1. AI meets BigData How to cross the chasm
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- 4. AI history àPerceptron 1958 F. Rosenblatt, “Perceptron” model, neuronal networks 1943 W. McCulloch, W. Pitts, “Neuron” as logical element OR function XOR function 1969 M. Minsky, S. Papert, triggers first AI winter feed forward
- 5. AI history àAI winter 1958 F. Rosenblatt, Perzeptron model, neuronal networks 1987-1993 the second AI winter, desktop computer, LISP machines expensive 1943 W. McCulloch, W. Pitts, neuron as logical element 1980 Boom expert systems, Q&A using logical rules, Prolog 1969 M. Minsky, S. Papert, trigger first AI winter 1993-2001 Moore’s law, Deep blue chess- playing, Standford DARPA challenge
- 6. 6 AI beats humanin games - 2016 Komodo beasts H. Nakamura in 2016AlphaGo beats L. Sedols in 2016 Go 4:1 Chess 2:1
- 7. Image Classification- 2016 HumanPerformance AI Performance https://arxiv.org/pdf/1602.07261.pdf 95% 97% The ability to understand the content of an image by using machine learning
- 8. Breast Cancer Diagnoses- 2017 Pathologist Performance AI Performance https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html 73% 92% Doctors often use additional tests to find or diagnose breast cancer The pathologist ended up spending 30 hours on this task on 130 slides A closeup of a lymph node biopsy.
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- 10. Structured data 80% ofworld’s data is unstructured
- 11. Fishing in thesea versus fishing in the lake Data Warehouse Data Lake Business Intellingence helps find answers to questions you know. Data Science helps you find the question itself. Any kind of data & schema-on-readStructured data & schema-on-write Parallel processing on big dataSQL-ish queries on database tables Extract, Transform, Load Extract, Load, Transform-on-the-fly Low cost on commodity hardwareExpensive for large data
- 12. More Data +Bigger Models Accuracy Scale (data size, model size) other approaches neural networks 1990s https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI
- 13. More Data +Bigger Models + More Computation Accuracy Scale (data size, model size) other approaches neural networks Now https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI more compute
- 14. More Data +Bigger Models + More Computation = Better Results in Machine Learning
- 15. Millions of “trip” eventseach day globally 400+ billion viewing- related events per day Five billion data points for Price Tip feature Movie recommendation Price optimization Routing and price optimization
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- 17. Single machineML specialistSmall data
- 18. Single machineML specialistSmall data Single machineML specialist Small data
- 19. Single machineML specialistSmall data Single machineML specialist Small data X X
- 20. Single machineML specialistBig data Single machineML specialist Big data X X
- 21. Train and evaluatemachine learning models at scale Single machine Data center How to run more experiments faster and in parallel? How to share and reproduce research? How to go from research to real products?
- 22. Distributed Machine Learning DataSize Model Size Model parallelism Single machine Data center Data parallelism training very large models exploring several model architectures, hyper- parameter optimization, training several independent models speeds up the training
- 23. Compute Workload forTraining and Evaluation I/O intensive Compute intensive Single machine Data center
- 24. I/O Workload forSimulation and Testing I/O intensive Compute intensive Single machine Data center
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- 27. The new risingstar
- 28. 11/24/17 28 TensorFlow Standalone TensorFlow On YARN TensorFlow Onmulti- colored YARN TensorFlow On Spark TensorFrames TensorFlow On Kubernetes TensorFlow On Mesos Distributed TensorFlow on Hadoop, Mesos, Kubernetes, Spark https://www.slideshare.net/jwiegelmann/distributed -tensorflow-on-hadoop-mesos-kubernetes-spark
- 29. Hidden Technical Debtin Machine Learning Systems https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf Google, 2015
- 30. Hidden Technical Debtin Machine Learning Systems https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf Google, 2015
- 31. http://stevenwhang.com/tfx_paper.pdf TFX: A TensorFlow-BasedProduction-Scale Machine Learning Platform Google, 2017
- 32. https://eng.uber.com/michelangelo/ Michelangelo: Uber’s MachineLearning Platform
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- 34. Pricing for 890,000real-time predictions w/o training AWS: Compute Fees + Prediction Fees = $8.40 + $96.44 = $104.84 per month Google: Prediction $0.10 per thousand predictions, plus $0.40 per hour = $377 per month Azure: Packages $0, $100,13, $1.000,06, $9.999,98 = $1.000 per month Q3, 2017
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- 36. Who are you? Whodo you know? What can you afford? Where are you? What have you purchased? What do you like? What content do you prefer? Why have you contacted us?
- 37. Marketing Tools Touchpoints Aftersales Capture Dataacross Digital Channels Each of these customer interactions produces data
- 38. Breaking Down DataSilos Connect all your data tools, other sources, and gain a 360 degree view on your data Get actionable insights and serve them personal, relevant content along their journey Real-time processing and decision making One Data Platform Marketing Tools Touchpoints Historical Aftersales Data Analytics Machine Learning Data Apps
- 39. Actionable data insightsthat businesses can use to... ü Better understand and better engage your customers ü Respond to the convergence of customer expectations ü Driver of brand perception 360 degree view of your customers Social Apps CRM Billing Channels Service Call Location Devices Network Ordering Customer 360
- 40. Where AI canhelp… + Predicting lifetime value + Churn estimation + Customer segmentation + Cross/Upselling + Recommendations + Demand forecasting + Market Basket Analysis + Sentiment analysis + Loyalty programs + Reactivation likelihood + Discount targeting + Call to action + Risk analysis + In store traffic patterns
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- 42. High-level Development Processfor Autonomous Vehicles 1 Collect sensors data 3 Autonomous Driving 2 Model Engineering Data Logger Control Unit Big Data Trained Model Data Center Agenda
- 43. Sensors Udacity LincolnMKZ Camera 3x Blackfly GigE Camera, 20 Hz Lidar Velodyne HDL-32E, 9.5 Hz IMU Xsens, 400 Hz GPS 2x fixed, 1 Hz CAN bus, 1,1 kHz Robot Operating System Data 3 GB per minute https://github.com/udacity/self-driving-car
- 44. Sensors Spec Sensor blinding, sunlight, darkness rain,fog, snow non-metal objects wind/ high velocity resolution range data Ultrasonic yes yes yes no + + + Lidar yes no yes yes +++ ++ + Radar yes yes no yes ++ +++ + Camera no no yes yes +++ +++ +++
- 45. Machine Learning 101 Observations State Estimation Modeling& Prediction Planning Controls f(x) Controls Observations
- 46. Machine Learning forAutonomous Driving + Sensor Fusion clustering, segmentation, pattern recognition + Road ego-motion, image processing and pattern recognition + Localization simultaneous localization and mapping + Situation Understanding detection and classification + Trajectory Planning motion planning and control + Control Strategy reinforcement and supervised learning + Driver Model image processing and pattern recognition
- 47. Machine Learning Cycle Datacollection for training/test Feature engineering I/O workload Model development and architecture Compute workload I/O workload Training and evaluation Re- Simulation and Testing Scaling and monitoring Model deployment versioning 1 2 3 Model tuning
- 48. Flux – OpenMachine Learning Stack Training & Test data Compute + Network + Storage Deploy model ML Development & Catalog & REST API ML-Specialists Feature Engineering Training Evaluation Re-Simulation Testing CaffeOnSpark Sample Model Prediction Batch Regression Cluster Dataset Correlation Centroid Anomaly Test Scores ü Mainly open source ü No vendor lock in ü Scale-out architecture ü Multi user support ü Resource management ü Job scheduling ü Speed-up training ü Speed-up simulation
- 49. Feature Engineering + HadoopInputFormat and Record Reader for Rosbag + Process Rosbag with Spark, Yarn, MapReduce, Hadoop Streaming API, … + Spark RDD are cached and optimized for analysis Ros bag Processing Engine Computer Network Storage Advanced Analytics RDD Record Reader RDD DataFrame, DataSet SQL, Spark APIs NumPy Ros Msg
- 50. Training & Evaluation +Tensorflow ROSRecordDataset + Protocol Buffers to serialize records + Save time because data conversion not needed + Save storage because data duplication not needed Training Engine Machine Learning Ros bag Computer Network Storage ROS Dataset Ros msg
- 51. Re-Simulation & Testing +Use Spark for preprocessing, transformation, cleansing, aggregation, time window selection before publish to ROS topics + Use Re-Simulation framework of choice to subscribe to the ROS topics Engine Re-Simulation with framework of choice Computer Network Storage Ros bag Ros topic core subscribe publish
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- 53. + Classification, Regression,Clustering, Collaborative Filtering, Anomaly Detection + Supervised/Unsupervised Reinforcement Learning, Deep Learning, CNN + Model Training, Evaluation, Testing, Simulation, Inference + Big Data Strategy, Consulting, Data Lab, Data Science as a Service + Data Collection, Cleaning, Analyzing, Modeling, Validation, Visualization + Business Case Validation, Prototyping, MVPs, Dashboards Data Science Machine Learning
- 54. + Architecture, DevOps,Cloud Building + App. Management Hadoop Ecosystem + Managed Infrastructure Services + Compute, Network, Storage, Firewall, Loadbalancer, DDoS, Protection + Continuous Integration and Deployment + Data Pipelines (Acquisition, Ingestion, Analytics, Visualization) + Distributed Data Architectures + Data Processing Backend + Hadoop Ecosystem + Test Automation and Testing Data Engineering Data Operations
- 55. Think Big BusinessStrategy Data Strategy Technology Strategy Agile Delivery Model Business Case Validation Prototypes, MVPs Data Exploration Data AcquisitionStart Small Value Proposition
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