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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
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
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
Our	Mission:
Make	Machine	Learning	Accessible	to	Everyone
Scientific	Advisory	Council
5
6H2O	Team
7H2O	Team
Arno	Candel,	CTO
Fortune’s	2014	Big	Data	All-Star
Sri	Ambati,	Co-founder	&	CEO
8H2O	Team
Origin	of	R	Package	`ggplot2`
9H2O	Team
Matt	Dowle
10H2O	Team
Erin	LeDell,	Chief	ML	Scientist
Women	in	ML/DS	& R-Ladies	Global
11H2O	Team
1st
4th
25th
48th
33rd
Their	Highest	Rank	in	Kaggle
(about	80,000	competitors)
12H2O	Team
1st
4th
25th
48th
33rd
181st
Trying	to	get	closer	to	them	at	some	point	…
13H2O	Team
Joe
Avni
Priya
Bonsoir!
14H2O	Team
H2O	Team	in	UK
Feb	2016	- Present
June	2017	- Present
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)
Joe’s	Real	Job	at	H2O.ai
16
Reminder:	#360Selfie
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
*	DATA	FROM	GOOGLE	ANALYTICS	EMBEDDED	IN	THE	END	USER	PRODUCT	
Worldwide
Community
Adoption
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.
CONFIDENTIAL
Platforms with H2O integration
H2O + KNIME Talk
at KNIME Summit
Mar 2017
H2O.ai Solution Leadership Across Verticals
21
2
1
Financial InsuranceMarketing TelecomHealthcareRetail Advisory	&	
Accounting
Community Expansion
Find	out	more:	www.h2o.ai/community/
23
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
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
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
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
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
H2O	Core
CPU
Model Building
H2O
H2O	Core
H2O H2O H2O
H2O	Core
CPU CPU CPU
Model Building
H2O Distributed In-Memory
H2O	Core
YARN
CPU CPU CPU
Model Building
H2O Distributed In-Memory
SQL NFS
S3
Firewall or Cloud
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
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
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
H2O	Flow	(Web)	– First	Demo
36
Using	H2O	with	R	and	Python	– Second	Demo
37
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
URL: docs.h2o.ai
Demo:	
H2O	on	a	320-Core	Hadoop	
Cluster
(Web	Interface)
40
41
https://www.kaggle.com/c/higgs-boson
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
11M Rows Size	(Raw):					7.48	GB
Compressed:	2.00	GB	(≈	27%	of	Raw)
44
10	nodes
10	x	32	=	
320	Cores
10	x		29.6	=	296	
GB	Memory
H2O	Water	Meter	
(CPU	Monitor)
45
10	x	32	=	320	Cores
Demo:	AutoML
46
Automatic	Machine	Learning	with	H2O
(R	Interface)
47Think 2018 / 3456 / March, 2018 / © 2018 IBM Corporation
48
AutoML
Think 2018 / 3456 / March, 2018 / © 2018 IBM Corporation
49
50
51
Learning	from	Boston	Housing	Data
52
Crime,
No.	of	rooms,
Age	…
Historical	House	Price Predicted	House	Price
H2O	AutoML:	
Learn	the	Pattern
53
54
55
56
57
58
59
Other	H2O	News
60
Latest	Developments
Events
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
“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
“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
https://github.com/h2oai/h2o4gpu
65
End	of	First	Talk
66
Any	Questions?
Making	Multimillion-Dollar	Decisions	
with	H2O	AutoML,	LIME	and	Shiny
My	journey	to	a	real	Moneyball	application
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
About	Moneyball
69
Billy	Beane Peter	Brand
(based	on	Paul	DePodesta)
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.
A	Proof-of-Concept	Demo	for	
IBM	Think	Conference
71
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
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
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
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
Shiny App
76
Presentation
Green: Predictions based
on Lahman only
Orange: Predictions based
on AriDB + Lahman
Think 2018 / 3456 / March, 2018 / © 2018 IBM Corporation
Acknowledgement
77
• 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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