H2O the Prediction Engine

Better predictions

https://github.com/0xdata/h2o

H2O makes hadoop do math

Hadoop = opportunity Not enough Data Scientists Analysts won’t code java

H2O the Prediction Engine Exploration Modeling Scoring

Big Data

H2O the Prediction Engine

Adhoc Exploration

Math Modeling

Real-time Scoring

Big Data Velocity Volume

H2O the Prediction Engine

Adhoc Exploration

Math Modeling

Real-time Scoring

Big Data

Messy Clustering

Classification

Ensembles

100’s nanos models

Regression

H2O the Prediction Engine

Big Data Exploration Modeling Scoring

Real-time

H2O the Prediction Engine

Big Data Exploration Modeling Scoring

Real-time

No New API

Approximate results each step

H2O the Prediction Engine

Big Data Exploration Modeling Scoring

Real-time

More Data beats Better Algorithms

H2O the Prediction Engine

Big Data Exploration Modeling Scoring

Real-time

More Data and Better Algorithms Scale & Parallelism

H2O the Prediction Engine

Big Data Exploration Modeling Scoring

Real-time

More Data and Better Algorithms Scale & Parallelism

fraud detection

Apps

reco engine

H2O the Prediction Engine

Intellectual Legacy

Math needs to be free

Open Source

Support and Innovation

https://github.com/0xdata/h2o

SriSatish Ambati, CEO & Co-founder Director of Engineering, DataStax, Cassandra & Hadoop Customers & Platform Marketing, Azul Cliff Click. CTO & Co-founder Chief JVM Architect, Azul, Sun, HP, Motorola, JIT & Hotspot Tomas Nykodym Phd Security, Intrusion Detection Cyprien Noel Founder ObjectFabric, TradeWeb, SmartTrade Michal Malohlava Phd DSLs, Compilers Jan Vitek Full Professor, Purdue, On Sabbatical, Real-time VM, R/stats Compiler

Kevin Normoyle AMD Fellow, Distinguished Engineer Sun, Consistency Models Tom Kraljevic VP Of Engineering, founder Luminix, Azul, PMC-Sierra, Chromatic

Credits & Team

Stephen Boyd Professor of Mathemat ica l Engineer ing, Stanford, Convex Opt

Trevor Hastie Professor of Stat is t ics, Stanford, General ized Addi t ive Models

Rob Tibshirani Professor of Stat is t ics, Stanford, GLMNet, Lasso

Doug Lea Mal loc for C. fork- jo in. java memory model , suny oswego Dhruba Borthakur HDFS, Hive, Facebook Nial l Dalton TimeSer ies DB, KX, High- f requency Trading, Cantor-F i tz Char les Zedlewski VP Products, Cloudera

Data Science & Advisors

Distributed! Extensible, reconfigurable!

Math-at Scale – Simple Legos

H2O

+ σ cov

*

µ mean

n

GLM Logistic

Regression

rand shuffle

histo gram

Random Decision

Trees

OLS

k-means

Volume: HDFS

HIVE/SQL

Data Scientist

Munging slice n dice Features

Classification Regression Clustering Optimal Model

Engineer

Velocity: Events Online Scoring

Exploration

Modeling

Offline Scoring

Business Analyst

Ensemble models Low latency

Applications

Predictions

Rule Engine

Before H2O

Product Road Map

algos: RandomForest GLM, ADMM, GLMnet, k-means data: dense, categorical api: REST, JSON, R-like console Scale, Single-Execution GridSearch

In 4-pilots

algos: GroupBy, Grep Unbalanced App: Fraud Detection data: sparse api: R, math, string Adhoc Analytics Multi-Execution Scoring Engine Event Ingest In production

algos: GBM, SVM, KNN Optimization App:RecoEngine data: sparse api: Tableau Visualization Multi-tenant Library Big Adoption

1.15.2013 5.15.2013 8.15.2013

secret sauce move code. not data

Linear Regression

fork/join. data partitioning. fine grain parallelism

phase 1 sums phase 2 distance phase 3 validate

arraylets leaf computes parent aggregates

company confidential. copyright 2012

Fraud Detection Scoring: Event stream on a ScoreCard Model Modeling: Random Forest for outlier detection Modeling: Event sequence patterns

Customer Behavior & Merchant Analytics Scoring: Purchase event stream scoring on Ensemble Models Modeling: Logistic Regression models for Customer Engagement

Failure Prediction from Sensor Data Model device failures and rank vendor graphs.

Upstream Oil Exploration Distance & Regression on 1TB big data MLS for Oil fields

Use Cases

Math & Hadoop users recommend us!

Data & Algorithms

SQL | HDFS | S3 | NoSQL

H2O – Real Time

REST

patterns sequences

Distributed Collections Execution

JSON R Excel

Java API

Hadoop Ecosystem

HDFS

H2O Map Reduce

Hive Pig

Impala Drill

Batch Interactive

H2O

•  Alternating Direction Method of Multipliers (Boyd) •  Decomposition-coordination •  Small Local Sub-Problems and Global Coordination

•  Broadcast & Gather •  Decomposability Dual Ascent + Convergence of Multipliers •  Block & Component Separability

•  Generalized Gradients (Hastie, Tibshirani, et al)

Generalized Linear Modeling

l1 norm regularization

https://github.com/0xdata/h2o/blob/master/src/main/java/hex/DLSM.java

•  Text Book implementation from Breiman’s paper.

•  Data is distributed upon ingest •  Splits on random selection of features

•  Gini & Entropy

•  Handle NAs (during training) •  Class-Weighting •  Stratified Sampling (local)

Random Forest

https://github.com/0xdata/h2o/tree/master/src/main/java/hex/rf

forest for the tree.. iris dataset

•  1% increase in predictive power - $11m @ major online payment system

•  Each fraud scored accurately = expected value of 10s of thousand dollars.

•  Leads cost $10-100/lead – Predicting accurate conversion and quality of leads goes directly to bottom line.

•  Competitive advantage in predicting which assets to acquire.

Models unlock value in data

Deployment - commodity / cloud

H2O

x86

H2O is pure java and easy-to-install

company confidential. copyright 2012

H2O

H2O

H2O the Prediction Engine

Better predictions

https://github.com/0xdata/h2o

H2O the Prediction Engine

Big Data Science Modeling & Scoring Engine Approximate results each step No new API

Use R, Excel & SAS Scale & Parallelism