H2O.ai Machine Intelligence

Robust, Tested and Supported Platform for Predictive Analytics

•  Founded:2011:Version3releasedin2015•  Product:H2Oopensourcein-memorypredic=onengine•  Team:50+Coredevelopersanddatascien=sts•  HQ:MountainView,CA.Sales:U.S.,U.K.&Canada

H2O.ai Overview

H2O.ai Machine Intelligence

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What is H2O?Opensourcein-memorypredic=onenginePlaMorm

• Parallelizedanddistributedalgorithmsmakingthemostuseoutofmul=threadedsystemsandgrids

• GLM,RandomForest,GBM,DeepLearning(ANN),GLRM,PCA,K-meansetc.

Data,PlaMormandClientAgnos=cAccess• Runsanywhere• RESTAPI–drivesH2OfromR,Python,WebUI,Excel,Tableau• Scorecodeforallmodels

Samecode.DifferentEnvironmentsScale• Useallofyourdatawithoutsubsebng• Nocodechangestogofromdevelopmenttoproduc=on

Single source of truth for R and Python users

Ensembles

Deep Neural Networks

Algorithms on H2O

•  Generalized Linear Models with L1 and L2 Penalties: Binomial, Gaussian, Gamma, Poisson and Tweedie

•  Naïve Bayes

•  Distributed Random Forest: Classification or regression models •  Gradient Boosting Machine: Produces an ensemble of

decision trees with increasing refined approximations

•  Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations

Supervised Learning

Statistical Analysis

Dimensionality Reduction

Anomaly Detection

Algorithms on H2O

•  K-means: Partitions observations into k clusters/groups of the same spatial size

•  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

•  Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning

Unsupervised Learning

Clustering

DataandClientAgnos/c

HDFS

S3

SQL

NoSQL

Classifica=onRegression

FeatureEngineering

In-Memory

MapReduce/ForkJoin

ColumnarCompression

DeepLearning

PCA,GLM,Cox

RandomForest/GBMEnsembles

H2OComputeEngine

Streaming

JavaScoreCode

MatrixFactoriza=on Clustering

Munging

H2O and R

Reading Data from Disk into H2O with R

STEP 1

R user

h2o_df = h2o.importFile(“Local/path/to/data.csv”)

Reading Data from Disk into H2O with R

Request data from disk

STEP 2

HTTP REST API request to

H2Ohas local file

path

2.2Initiate parallel

ingest

2.3

Disk 2.4

h2o.importFile()

2.1R function

call

Local H2O Instance

H2O

data.csv

Reading Data from Disk into H2O with R

STEP 3

h2o_df object created in R

Disk

Local HostPointer to Data

Return pointer to data in REST API

JSON Response

Disk provides

data

3.3

3.4 3.1

data.csv

h2o_df

3.2Parallelized

H2OFrame in DKV

Local H2O Instance

H2O Frame

H2O