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Latest Developments in H2O
This document provides an overview of latest developments at H2O.ai, including enhancements to their machine learning platform H2O such as Deep Water for distributed deep learning using GPUs, xgboost integration, stacked ensembles, automatic machine learning, and model interpretation capabilities. It also discusses H2O's community involvement through meetup groups and conferences like PyData.
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Latest Developments in H2O
- 1. Latest Developments inH2O Jo-fai (Joe) Chow Data Scientist joe@h2o.ai @matlabulous H2O at Booking.com – Amsterdam 6th April, 2017
- 2. About Me • Civil(Water) Engineer • 2010 – 2015 • Consultant (UK) • Utilities • Asset Management • Constrained Optimization • Industrial PhD (UK) • Infrastructure Design Optimization • Machine Learning + Water Engineering • Discovered H2O in 2014 • Data Scientist • 2015 • Virgin Media (UK) • Domino Data Lab (Silicon Valley, US) • 2016 – Present • H2O.ai (Silicon Valley, US) 2
- 3. About Me 3 R +H2O + Domino for Kaggle Guest Blog Post for Domino & H2O (2014) • The Long Story • bit.ly/joe_kaggle_story
- 4. About Me 4 Developing RPackages for Fun rPlotter (2014)
- 5. Agenda • About H2O.ai •Company • Machine Learning Platform • What’s New? • Deep Water • H2O + xgboost • Stacked Ensembles • Auto Machine Learning • Model Interpretation • Community • PyData Conference 5
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- 7. Company Overview Founded 2011Venture-backed, debuted in 2012 Products • H2O Open Source In-Memory AI Prediction Engine • Sparkling Water • Steam Mission Operationalize Data Science, and provide a platform for users to build beautiful data products Team 70 employees • Distributed Systems Engineers doing Machine Learning • World-class visualization designers Headquarters Mountain View, CA 7
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- 13. H2O Machine LearningPlatform 13
- 14. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O ComputeEngine 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 14
- 15. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O ComputeEngine 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 15 Import Data from Multiple Sources
- 16. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O ComputeEngine 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 16 Fast, Scalable & Distributed Compute Engine Written in Java
- 17. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O ComputeEngine 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 17 Fast, Scalable & Distributed Compute Engine Written in Java
- 18. Supervised Learning • GeneralizedLinear 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 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 18
- 19. H2O Deep Learningin Action 19
- 20. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O ComputeEngine 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 20 Multiple Interfaces
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- 23. 23 H2O Flow (Web)Interface
- 24. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O ComputeEngine 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 24 Export Standalone Models for Production
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- 28. Deep Water Next-Gen DistributedDeep Learning with H2O H2O integrates with existing GPU backends for significant performance gains One Interface - GPU Enabled - Significant Performance Gains Inherits All H2O Properties in Scalability, Ease of Use and Deployment Recurrent Neural Networks enabling natural language processing, sequences, time series, and more Convolutional Neural Networks enabling Image, video, speech recognition Hybrid Neural Network Architectures enabling speech to text translation, image captioning, scene parsing and more Deep Water 28
- 29. Deep Water Architecture Node1 Node N Scala Spark H2O Java Execution Engine TensorFlow/mxnet/Caffe C++ GPU CPU TensorFlow/mxnet/Caffe C++ GPU CPU RPC R/Py/Flow/Scala client REST API Web server H2O Java Execution Engine grpc/MPI/RDMA Scala Spark 29
- 30. Available Networks inDeep Water • LeNet • AlexNet • VGGNet • Inception (GoogLeNet) • ResNet (Deep Residual Learning) • Build Your Own 30 ResNet
- 31. 31 Example: Deep Water+ H2O Flow Choosing different network structures
- 32. 32 Choosing different backends (TensorFlow,MXNet, Caffe)
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- 38. Available in bothPython and R 38 https://github.com/woobe/odsc_h2o_machine_learning
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- 44. Time Series inH2O 44
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- 48. Sparkling Water H2O’s integrationwith Spark – latest developments 48
- 49. External H2O Backendfor Sparkling Water • High Availability Mode • Separating Spark and H2O • while preserving same API • Advantages • H2O does not crash when Spark executor goes down • Better resource management since resources can be planned per tool 49 • Disadvantages • Transfer overhead between Spark and H2O processes • Under measurement with cooperation of a customer • Links • https://github.com/h2oai/sparklin g- water/blob/master/doc/backends. md
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- 52. H2O Meetup Groups •www.meetup.com/topics/h2o/all/ • Need your help ☺ 52
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- 56. 56 H2O Tutorial Friday –4:00 pm (All Materials will be available on GitHub) Sparkling Water Talk Saturday – 3:15 pm Lucas’ Talk Saturday – 1:30 pm
- 57. • Organizers &Sponsors • Find us at PyData Conference • Live Demos 57 Thanks! • 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)