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Deep	Learning	is	Like	Water:	It's	Everywhere!
Arno	Candel,	PhD	
CTO,	H2O.ai	
@ArnoCandel



AI	By	The	Bay,	San	Francisco	
March	6,	2017
Meet	the	H2O	Makers
3
Software	Product:	H2O	-	AI	for	Business	Transformation	
• Distributed	Data	Frame	with	Scalable	Execution	Engine	
• Distributed	Algorithms	Deep	Learning,	GBM,	RF,	GLM,	K-Means,	PCA,	…	
• Apache	v2	Open	Source	(github.com/h2oai)	


H2O	is	Easy	to	Use	and	Deploy	
• h2o.ai/download	and	run	anywhere,	immediately	
• Client	APIs:	R,	Python,	Java,	Scala,	REST,	Flow	GUI	
• Spark	(cf.	Sparkling	Water),	Hadoop,	Bare	Metal	
• Productionize	with	auto-generated	Java/C++	scoring	code
H2O.ai	-	Makers	of	H2O,	Sparkling	Water,	Deep	Water,	…
https://www.cbinsights.com
H2O.ai	-	At	the	Core	of	AI
H2O.ai	-	Loved	By	The	Best
H2O.ai	-	Visionary	in	2017	Gartner	MQ	for	Data	Science
H2O	Deep	Water	got	(Tech)	Crunch’ed
H2O.ai	-	Highlighted	by	Fortune	Magazine
http://fortune.com/2017/02/23/artificial-intelligence-companies/
Powerful,	Scalable	
Techniques	for	Deep	
Learning	and	AI
brand	new:	Dec	2016
H2O	Book	-	Written	by	the	Community
H2O.ai	Customer	Love
10
http://www.h2o.ai/customers/
11
http://www.h2o.ai/customers/
H2O.ai	Customer	Love
12
High	Level	Architecture	of	H2O
HDFS
S3
NFS
Distributed	
In-Memory
Parallel	Parser
Lossless	
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:

Standalone	Scoring	Code
C/C++/Java

R/Py/etc.
Data	Prep	Export:	
Plain	Old	Java	Object	
Local
SQL
LDAP
Kerberos
SSL
HTTPS
HTTP
Native	APIs:	Java,	Scala	—	REST	APIs:	R,	Python,	Flow,	JavaScript,	Java
13
library(h2o)	
h2o.init()	
h2o.deeplearning(x=1:4,y=5,as.h2o(iris))
import	h2o	
from	h2o.estimators.deeplearning	import	H2ODeepLearningEstimator	
h2o.init()	
dl	=	H2ODeepLearningEstimator()	
dl.train(x=list(range(1,4)),	y="Species",	training_frame=iris.hex)
import	_root_.hex.deeplearning.DeepLearning	
import	_root_.hex.deeplearning.DeepLearningParameters	
val	dlParams	=	new	DeepLearningParameters()	
dlParams._train	=	iris.hex	
dlParams._response_column	=	‘Species	
val	dl	=	new	DeepLearning(dlParams)	
val	dlModel	=	dl.trainModel.get
All	heavy	lifting	is	done	by	the	backend!
Built-in	interactive	GUI	and	
notebook	-	no	coding	needed!
Live	Demo	of	Distributed	Deep	Learning	in	H2O
Airline dataset: 116M flights in the U.S. over 20 years
10x	in-memory	compression	vs	CSV All	cluster	CPU	cores	are	busy
Brand-new:	H2O	XGBoost	Integration	(Gradient	Boosting)
Why	XGBoost?	
Competitive	accuracy	and	speed	(great	for	Kaggle)	
GPU	support	(for	small/medium	data)	
Efficient	on	sparse	data	
Why	integrate	into	H2O?	
Ease	of	use	(Flow	GUI,	R/Py	APIs)	
Real-time	model	status	(var	imp,	metrics)	
Efficient	data	preprocessing	(sparse,	categorical)	
Integration	into	H2O	ecosystem	(modeling,	deployment,	support)
Live	Demo	of	GPU	Gradient	Boosting	in	H2O
Kaggle dataset: BNP Paribas Cardif Claims Management
Deep	Water	=	THE	Deep	
Learning	Platform
H2O	integrates	the	top

open-source	DL	tools
Native	GPU	support 																																						is	up	to	100x	faster	than
Enterprise	Ready
Easy	to	train	and	deploy,	interactive,	scalable,	etc.	
Flow,	R,	Python,	Spark/Scala,	Java,	REST,	POJO,	Steam
New	Big	Data	Use	Cases	
(previously	impossible	or	
difficult	in	H2O)
Image	-	social	media,	manufacturing,	healthcare,	…	
Video	-	UX/UI,	security,	automotive,	social	media,	…	
Sound	-	automotive,	security,	call	centers,	healthcare,	…	
Text	-	NLP,	sentiment,	security,	finance,	fraud,	…	
Time	Series	-	security,	IoT,	finance,	e-commerce,	…
Deep	Water:	Best	Open-Source	Deep	Learning
Enterprise	Deep	Learning	for	Business	Transformation
Deep	Water	Brings	State-Of-The-Art	Deep	Learning	on	GPUs	to	H2O
H2O	Deep	Learning:	
simple	multi-layer	networks,	CPUs
H2O	Deep	Water:

arbitrary	networks,	CPUs	or	GPUs
Limited	to	business	analytics,	
statistical	models	(CSV	data)
Large	networks	for	big	data	
(e.g.	image	1000x1000x3	->	3m	inputs	per	observation)
1-5	layers	
MBs/GBs	of	data
1-1000	layers	
GBs/TBs	of	data
Open-Source	-	Leverage	Community	Code,	Data	and	Models
Best	Image	Classifier	as	of	Aug	2016:	Google	+	Microsoft	Hybrid	Architecture
https://research.googleblog.com/2016/08/improving-inception-and-image.html
open-source	implementation
H2O	takes	DL	model	definition	as	input
Build	your	own	models	with	Deep	Water	Today!	
https://github.com/h2oai/h2o-3/blob/master/h2o-py/tests/testdir_algos/deepwater/pyunit_inception_resnet_v2_deepwater.py
Live	Demo	of	Deep	Water	for	Image	Classification	on	GPUs
Yesterday:	Small	Data	(<GB) Today:	Big	Data	(TeraBytes,	ExaBytes)
Data	+	Skills

are	good	for	business
Data	+	Machine	Learning	
ARE	the	business
Things	are	Changing	Quickly
Challenges	With	AI	and	Deep	Learning
CEO:				“We	will	transform	our	business	with	AI”	
Management:				“Hire	someone	to	give	us	AI”	
Senior	Data	Scientist:				“I	should	look	into	AI”	
Junior	Data	Scientist:				“I	use	TensorFlow	all	the	time”	
High	School	Kid:				“I	did	my	internship	on	Deep	Learning”	
Average	Joe:				“I	want	a	self-driving	car	(and	keep	my	job)”	
Stanford	Professors:		
“focus	on	interpretability,	start	with	simple	models!”
The	Hype	and	Reality	of	AI
H2O.ai	Stanford	Advisors
stankrd	pic
Sri/CEO									Boyd							Hastie											Tibshirani
in	a	conference	room	not	so	far	away
me
Which	Open-Source	AI	Platform	to	Use?
Which	Programming	Language	To	Use?
Which	one	for	Development	vs	Production?
Which	Hardware	To	Use?
Which	one	for	Development	vs	Production?
Analog/Neuromorphic
Who	Does	the	Work	and	on	What	Infrastructure?
Which	one	for	Development	vs	Production?
Cloud?	Which? On	Premise?
Data	Lake?	
Micro-Services?
Which	one	for	Development	vs	Production?
When	is	the	Model	Good	Enough?
Crowd	sourcing? Trust	a	Genius? Internal	Bake-Off?
What	problem	are	you	solving	in	the	first	place?
What	problem	should/could	you	be	solving	instead?
What	can	you	learn	from	the	model?
How	can	you	improve	the	models?	More,	better	data?
How	can	you	characterize	the	model?
Do	you	need	AI,	Deep	Learning	or	just	a	simple	model?
Back	to	the	Drawing	Board!
Gradient	Boosting

Machine
Generalized

Linear	Modeling
Deep	Learning
Distributed

Random	Forest
Do	you	need	AI,	Deep	Learning	or	just	a	Simple	Model?
Future	Of	AI:			Or	What’s	Left	for	Humans	to	Do?
Charlie	Chaplin	-	Modern	Times	1936

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