Scalable and Automatic Machine Learning with H2O

1. Scalable and Automatic Machine Learning with H2O Introduction, demos and a real-world use-case Jo-fai (Joe) Chow Data Scientist / Community Manager joe@h2o.ai @matlabulous

2. Agenda • Talk 1: Introduction to H2O • Company and People • H2O Open Source ML Platform • Demos • H2O on Hadoop (320 Cores) • AutoML • Other News • Talk 2: Moneyball • From a proof-of-concept project to a multimillion-dollar contract 2

3. Founded 2012, Series C in Nov, 2017 Products • Driverless AI – Automated Machine Learning • H2O Open Source Machine Learning • Sparkling Water Mission Democratize AI. Do Good Team ~100 employees • Distributed Systems Engineers doing Machine Learning • World-class visualization designers Offices Mountain View, London, Prague 3 Company Overview

4. 4 Our Mission: Make Machine Learning Accessible to Everyone

5. Scientific Advisory Council 5

6. 6H2O Team

7. 7H2O Team Arno Candel, CTO Fortune’s 2014 Big Data All-Star Sri Ambati, Co-founder & CEO

8. 8H2O Team Origin of R Package `ggplot2`

9. 9H2O Team Matt Dowle

10. 10H2O Team Erin LeDell, Chief ML Scientist Women in ML/DS & R-Ladies Global

11. 11H2O Team 1st 4th 25th 48th 33rd Their Highest Rank in Kaggle (about 80,000 competitors)

12. 12H2O Team 1st 4th 25th 48th 33rd 181st Trying to get closer to them at some point …

13. 13H2O Team Joe Avni Priya Bonsoir!

14. 14H2O Team H2O Team in UK Feb 2016 - Present June 2017 - Present

15. Joe’s Roles at H2O.ai 15 • Data Scientist / Sales Engineer / Speaker / Meetup Organiser / Community Evangelist (on paper) • Unofficial Photographer of H2O.ai SWAG (the travelling data scientist) • H2O.ai SWAG EMEA Distributor (please help yourself)

16. Joe’s Real Job at H2O.ai 16 Reminder: #360Selfie

17. H2O Products In-Memory, Distributed Machine Learning Algorithms with H2O Flow GUI H2O AI Open Source Engine Integration with Spark Lightning Fast machine learning on GPUs Automatic feature engineering, machine learning and interpretability Secure multi-tenant H2O clusters

18. * DATA FROM GOOGLE ANALYTICS EMBEDDED IN THE END USER PRODUCT Worldwide Community Adoption

19. CONFIDENTIAL Gartner names H2O as Leader with the most completeness of vision • H2O.ai recognized as a technology leader with most completeness of vision • H2O.ai was recognized for the mindshare, partner network and status as a quasi-industry standard for machine learning and AI. • H2O customers gave the highest overall score among all the vendors for sales relationship and account management, customer support (onboarding, troubleshooting, etc.) and overall service and support.

20. CONFIDENTIAL Platforms with H2O integration H2O + KNIME Talk at KNIME Summit Mar 2017

21. H2O.ai Solution Leadership Across Verticals 21 2 1 Financial InsuranceMarketing TelecomHealthcareRetail Advisory & Accounting

22. Community Expansion Find out more: www.h2o.ai/community/

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24. H2O Products In-Memory, Distributed Machine Learning Algorithms with H2O Flow GUI H2O AI Open Source Engine Integration with Spark Lightning Fast machine learning on GPUs Automatic feature engineering, machine learning and interpretability Secure multi-tenant H2O clusters In-Memory, Distributed Machine Learning Algorithms with H2O Flow GUI

25. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 25

26. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 26 Import Data from Multiple Sources

27. Supported Formats & Data Sources CSV XLS XLSX ORC* Hive* SVMLight ARFF Parquet Avro 1.8.0* HDFS S3 NFS LOCAL SQL 9Formats 5Sources File type or Folder of Files * 1. only if H2O is running as a Hadoop job * 2. Hive files that are saved in ORC format * 3. without multi-file parsing or column type modification

28. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 28 Fast, Scalable & Distributed Compute Engine Written in Java

29. H2O Core CPU Model Building H2O

30. H2O Core H2O H2O H2O

31. H2O Core CPU CPU CPU Model Building H2O Distributed In-Memory

32. H2O Core YARN CPU CPU CPU Model Building H2O Distributed In-Memory SQL NFS S3 Firewall or Cloud

33. Distributed Algorithms • Foundation for In-Memory Distributed Algorithm Calculation - Distributed Data Frames and columnar compression • All algorithms are distributed in H2O: GBM, GLM, DRF, Deep Learning and more. Fine-grained map-reduce iterations. • Only enterprise-grade, open-source distributed algorithms in the market User Benefits Advantageous Foundation • “Out-of-box” functionalities for all algorithms (NO MORE SCRIPTING) and uniform interface across all languages: R, Python, Java • Designed for all sizes of data sets, especially large data • Highly optimized Java code for model exports • In-house expertise for all algorithms Parallel Parse into Distributed Rows Fine Grain Map Reduce Illustration: Scalable Distributed Histogram Calculation for GBM Foundation for Distributed Algorithms 33

34. Supervised Learning • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations H2O-3 Algorithms Overview Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning 34

35. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 35 Multiple Interfaces

36. H2O Flow (Web) – First Demo 36

37. Using H2O with R and Python – Second Demo 37

38. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 38 Export Standalone Models for Production

39. 39 URL: docs.h2o.ai

40. Demo: H2O on a 320-Core Hadoop Cluster (Web Interface) 40

41. 41 https://www.kaggle.com/c/higgs-boson

42. Learning from Higgs Boson Machine Data 42 Sensors (Detector) Data Historical Outcome Is it a Higgs Particle (Yes/No) Predicted Outcome Learn the Pattern 11M Rows 28 Features Raw Data Size: 7.48 GB

43. 43 11M Rows Size (Raw): 7.48 GB Compressed: 2.00 GB (≈ 27% of Raw)

44. 44 10 nodes 10 x 32 = 320 Cores 10 x 29.6 = 296 GB Memory

45. H2O Water Meter (CPU Monitor) 45 10 x 32 = 320 Cores

46. Demo: AutoML 46 Automatic Machine Learning with H2O (R Interface)

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52. Learning from Boston Housing Data 52 Crime, No. of rooms, Age … Historical House Price Predicted House Price H2O AutoML: Learn the Pattern

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60. Other H2O News 60 Latest Developments Events

61. H2O Products In-Memory, Distributed Machine Learning Algorithms with H2O Flow GUI H2O AI Open Source Engine Integration with Spark Lightning Fast machine learning on GPUs Automatic feature engineering, machine learning and interpretability Secure multi-tenant H2O clusters Lightning Fast machine learning on GPUs

62. “Confidential and property of H2O.ai. All rights reserved” Supervised Learning • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Algorithms on H2O-3 (CPU) Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning

63. “Confidential and property of H2O.ai. All rights reserved” Supervised Learning • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Algorithms on H2O4GPU (more to come) Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning

64. 64 https://github.com/h2oai/h2o4gpu

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66. End of First Talk 66 Any Questions?

67. Making Multimillion-Dollar Decisions with H2O AutoML, LIME and Shiny My journey to a real Moneyball application

68. About Moneyball The first rule of Moneyball: You do not ask me about the names of team and player involved. The second rule of Moneyball: You do not ask me about the names of team and player involved. (… for legal reasons …) The third rule of Moneyball: If you happen to guess the names right, I can neither confirm nor deny. 68

69. About Moneyball 69 Billy Beane Peter Brand (based on Paul DePodesta)

70. Ari Kaplan – the Real ”Moneyball” Guy 70 • The real characters in the movie (Billy Beane and Paul DePodesta) did not want to work with Hollywood. • The filmmaker interviewed Ari instead and created the Paul DePodesta character based on Ari’s real-life story. • Ari happens to work at Aginity so we have a real ”Moneyball” guy for this demo.

71. A Proof-of-Concept Demo for IBM Think Conference 71

72. Enterprise Solution 72Think 2018 / 3456 / March, 2018 / © 2018 IBM Corporation The Architecture The Workflow 1. Data loaded into the databases 2. Connected diverse data sources to Amp 3. Amp used to create derived attributes and publish them and data to DSX and H2O 4. DSX and H2O to build and tweak statistical and machine learning models 5. Visualizations tested in Immersive Insights 6. Steps 4 and 5 repeated to get settled data 7. Statistical and machine learning models saved in Amp 8. Data exported to Immersive Insights for final visualizations DB2 Machine Learning & AI Libraries Data Science Modeling Tools Augmented Reality Visualization Analytic Management and Reuse Layer Hadoop Data Environment High-performing Database for Analytics

73. Approach One: Learning from Lahman only Lahman: Age, Height, Weight … Historical Performance Stats Home Runs Batting Average … Predictions H2O AutoML: Learn the Pattern Sliding Windows (Stats from previous n years) About 300 Lahman Features 73

74. Approach Two: Learning from Lahman & AriDB Lahman: Age, Height, Weight … Historical Performance Stats Home Runs Batting Average … Predictions H2O AutoML: Learn the Pattern Sliding Windows (Stats from previous n years) About 300 Lahman Features + 200 AriDB Features AriDB: Fastball, curveball, slider, velocity … 74

75. Timeline • March 19 – AutoML Predictions finalized. Initial presentation in Excel. • March 20 – Version 1 of Shiny app. Ari used to app to validate some players he had in mind and recommended one player to his team. • March 21 – Multimillion-dollar contract finalized. • March 22 – Moneyball presentation at IBM Think 75

77. Acknowledgement 77

78. • Organisers & Sponsors • Alexia Audevart • Christophe Regouby • HarryCow Coworking • H2O’s Mission • Democratize AI • Make Machine Learning Accessible to Everyone 78 Merci beaucoup! • Code, Slides & Documents • bit.ly/h2o_meetups • docs.h2o.ai • Contact • joe@h2o.ai • @matlabulous • github.com/woobe • Please search/ask questions on Stack Overflow • Use the tag `h2o` (not h2 zero)

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