This document provides an overview of machine learning and artificial intelligence presented by Arno Candel, Chief Architect at H2O.ai. It discusses the history and evolution of AI from early concepts in the 1950s to recent advances in deep learning. It also describes H2O.ai's platform for scalable machine learning and how it works, allowing users to easily build and deploy models on big data using APIs for R, Python, and other languages.

1. H2O.ai
Machine Intelligence
H2O: A Platform for Big Math
Arno Candel, PhD
Chief Architect
or: How to make A.I. and TensorFlow work for you
+20 more

2. H2O.ai
Machine Intelligence
Who Am I?
Arno Candel
Chief Architect,
Physicist & Hacker at H2O.ai
PhD Physics, ETH Zurich 2005
10+ yrs Supercomputing (HPC)
6 yrs at SLAC (Stanford Linear Accelerator)
4.5 yrs Machine Learning
2.5 yrs at H2O.ai
Fortune Magazine Big Data All Star
Follow me @ArnoCandel
2
Who am I?

3. H2O.ai
Machine Intelligence 3
Overview
Machine Learning (ML)
Artificial Intelligence (A.I.)
Computer Science (CS)
H2O.ai
Deep Learning (DL)
hot hot hot hot hot

4. H2O.ai
Machine Intelligence 4
A Simple Deep Learning Model: Artificial Neural Network
heartbeat
blood
pressure
oxygen
send to regular care
send to intensive
care unit (ICU)
IN: data OUT: prediction
nodes : neuron activations (real numbers) — represent features
arrows : connecting weights (real numbers) — learned during training
: non-linearity x -> f(x) — adds model complexity
from 1970s, now rebranded as DL

5. H2O.ai
Machine Intelligence 5
Brief History of A.I., ML and DL
John McCarthy
Princeton, Bell Labs, Dartmouth, later: MIT, Stanford
1955: “A proposal for the Dartmouth summer
research project on Artificial Intelligence”
with Marvin Minsky (MIT), Claude Shannon
(Bell Labs) and Nathaniel Rochester (IBM)
http://www.asiapacific-mathnews.com/04/0403/0015_0020.pdf
A step back: A.I. was coined over 60 years ago

6. H2O.ai
Machine Intelligence 6
Step 1: Great Algorithms + Fast Computers
http://nautil.us/issue/18/genius/why-the-chess-computer-deep-blue-played-like-a-human
1997: Playing Chess
(IBM Deep Blue beats Kasparov)
Computer Science
30 custom CPUs, 60 billion moves in 3 mins
“No computer will ever beat me at playing chess.”

7. H2O.ai
Machine Intelligence 7
Step 2: More Data + Real-Time Processing
http://cs.stanford.edu/group/roadrunner/old/presskit.html
2005: Self-driving Cars
DARPA Grand Challenge, 132 miles
(won by Stanford A.I. lab*)
Sensors & Computer Science
video, radar, laser, GPS, 7 Pentium computers
“No computer will ever drive a car!?”
*A.I. lab was established by McCarthy et al. in the early 60s

8. H2O.ai
Machine Intelligence 8
Step 3: Big Data + In-Memory Clusters
2011: Jeopardy (IBM Watson)
In-Memory Analytics/ML
4 TB of data (incl. wikipedia), 90 servers,
16 TB RAM, Hadoop, 6 million logic rules
https://www.youtube.com/watch?v=P18EdAKuC1U https://en.wikipedia.org/wiki/Watson_(computer)
Note: IBM Watson received the question in electronic written form, and was
often able to press the answer button faster than the competing humans.
“No computer will ever answer random questions!?”

9. H2O.ai
Machine Intelligence 9
“No computer will ever speak any language!?”
2014: Google
(acquired Quest Visual)
Deep Learning
Convolutional and Recurrent
Neural Networks,
with training data from users
Step 4: Deep Learning
• Translate between 103 languages by typing
• Instant camera translation: Use your camera to translate text instantly in 29 languages
• Camera Mode: Take pictures of text for higher-quality translations in 37 languages
• Conversation Mode: Two-way instant speech translation in 32 languages
• Handwriting: Draw characters instead of using the keyboard in 93 languages

10. H2O.ai
Machine Intelligence 10
Step 5: Augmented Deep Learning
2014: Atari Games (DeepMind)
2016: AlphaGo (Google DeepMind)
Deep Learning
+ reinforcement learning, tree search,
Monte Carlo, GPUs, playing against itself, …
https://deepmind.com
Go board has approx.
200,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 (2E170) possible positions.
trained from raw pixel values, no human rules
“No computer will ever beat the best Go master!?”

11. H2O.ai
Machine Intelligence 11
Microsoft had won the Visual Recognition challenge:
http://image-net.org/challenges/LSVRC/2015/
Step 6: A.I. Chatbots have Opinions too!

12. H2O.ai
Machine Intelligence 12
What Will Change?
Today Tomorrow
Better Data — Better Models — Better Results
Example:
Fraud
Prediction

13. H2O.ai
Machine Intelligence 13
H2O.ai - Makers of H2O
H2O - AI for Business Transformation
• Scalable and Distributed Data Science and Machine Learning:
Deep Learning, Gradient Boosting, Random Forest, Decision Trees,
Logistic Regression, Generalized Linear Modeling, K-Means, PCA, GLRM, …
• Fast, accurate, robust, proven, fully featured
• Apache v2 open source (github.com/h2oai)
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, Standalone
• Java scoring code auto-generated

14. H2O.ai
Machine Intelligence 14
H2O.ai - Growing Rapidly
+ 10 more recent hires
author
data.table
r2d3.us
ceo
grammar
of graphics
pure
software
kaggle
master
many many talents at H2O…
found Pentium bug
POSIX
h2o.ai/careers

15. H2O.ai
Machine Intelligence 15
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:
Plain Old Java Object
Your
Imagination
Data Prep Export:
Plain Old Java Object
Local
SQL
LDAP
Kerberos
SSL
HTTPS
HTTP

16. H2O.ai
Machine Intelligence
Native APIs: Java, Scala — REST APIs: R, Python, Flow, JavaScript, Java
16
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 necessary!

17. H2O.ai
Machine Intelligence 17
Gradient Boosting Machine
Tree Model
(nano-fast)
Auto-generated
Java scoring code
to easily
Operationalize Data Science
Easily Bring Models into Production
READ MORE

18. H2O.ai
Machine Intelligence
Spark + H2O = Sparkling Water
18
• Spark 2.0 API compatibility
• Use H2O algorithms in
conjunction with, or instead of,
MLLib algorithms on Spark
• Build Ensembles using H2O and
MLLib Algorithms
• Visual Intelligence for Spark. Run
Spark, MLLib, Scala in Flow
• Export MLLib models as POJOs
• Toolchain for ML pipelines and
debugging support
Sparkling W
ater
2.0

19. H2O.ai
Machine Intelligence 19
Live H2O Deep Learning Demo: Predict Airplane Delays
10 nodes:
all 320 cores busy
real-time, interactive
model inspection in Flow
116M rows, 6GB CSV file
800+ predictors (numeric + categorical)
model trained in <1 min:
2M+ samples/second
Deep Learning Model

20. H2O.ai
Machine Intelligence 20
H2O Elastic Net (GLM): 10 secs
alpha=0.5, lambda=1.379e-4 (auto)
H2O Deep Learning: 45 secs
4 hidden ReLU layers of 20 neurons, 1 epoch
Features have non-
linear impact
Chicago, Atlanta,
Dallas:
often delayed
Significant Performance Gains with Deep Learning
Predict departure delay (Y/N) on 20 years of airline flight data
(116M rows, 12 cols, categorical + numerical data with missing values)
WATCH NOW
AUC: 0.656
AUC: 0.703
(higher is better, ranges from 0.5 to 1)
Feature importances
10 nodes: Dual E5-2650 (8 cores, 2.6GHz), 10GbE

21. 21
• Data matrix is chunked into columnar blocks
• Algorithms can parallelize over these blocks
• Scalable to many TBs: Each node fills its memory with data
• Columns are separate entities (fast add/remove/modify)
• Similar to data frames in R, Pandas, and now also Spark
Distributed In-Memory Data Frames
So How Does It Work?

22. 22
p cols
N/6 rows
N/6 rows
N/6 rows
N/6 rows
N/6 rows
N/6 rows
massively
p cols
N
rows
parallel
Parallel Parse into Distributed Rows
HDFS, S3, NFS, SQL, …
parser

23. 23
map()
map() reduce()
reduce()
reduce()map()
map()
map()
map() reduce()
reduce()
reduce()map()
map()
map()
map() reduce()
reduce()
reduce()map()
map()
map()
map() reduce()
reduce()
reduce()map()
map()
map()
map() reduce()
reduce()
reduce()map()
map()
map()
map() reduce()
reduce()
reduce()map()
map()
reduce()
reduce()
reduce()
reduce()
reduce()driver Algo calls
M/R Task
Data Parallelism - all CPU cores are at work
Compute Paradigm: Fine-Grain Map/Reduce
final result
map(): process data, reduce(): aggregate results

24. 24
• Distributed in-memory data store holds data, models, etc.
• Columnar compression (often better than gzip on disk)
• Low-level Java code (byte[], float[], bit operations, etc.)
• Data read/write access at memory bandwidth speeds
• Custom serialization, networking and execution layer
• Auto-generated REST client-server API (R, Python, Flow,…)
• Standalone scoring code auto-generated for every model
Implementation Details

25. 25
Distributed Gradient Boosting Machine
find optimal split
(feature & value)
• H2O: First open-source implementation of scalable, distributed
Gradient Boosting Machine - fully featured
• Parallelized Individual Tree Construction
• Discretization (binning) for speedup without loss of accuracy
age < 25 ?
Y N
all data
age
12 118
income
1k 1M
Analytical error landscape
best split: age 25
H2O: discretized into bins
12 118
age 25
age

26. 26
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
map()
driver Algo calls
histogram
method
data parallelism - global histogram computation
Scalable Distributed Histogram Calculation
global histogram
local histogram - one each for w, w*y and w*y2
w: observation weights
y : response
Same results as if
computed on a
single compute
node

27. Over 7000 enterprises use H2O
Financial Insurance MarketingTelecom Healthcare
27

28. H2O.ai
Machine Intelligence
User Based Insurance
WATCH NOW
WATCH NOW
“H2O is an enabler in how people
are thinking about data.”
“We have many plans to use H2O
across the different business units.”
28
Today’s Keynote!

29. H2O.ai
Machine Intelligence
Digital Marketing - Campaigns
“H2O gave us the capability to do Big
Modeling. There is no limit to scaling in H2O.”
“Working with the H2O
team has been amazing.”
“The business value that we have gained
from advanced analytics is enormous.”
WATCH NOW
WATCH NOW
29

30. H2O.ai
Machine Intelligence
WATCH NOW
WATCH NOW
Matching TV Watching Behavior with Buying Behavior
“Unlike other systems where I had
to buy the whole package and just
use 10-20%, I can customize H2O
to suit my needs.”
“I am a big fan of open source. H2O is
the best fit in terms of cost as well as ease
of use and scalability and usability.”
30

31. H2O.ai
Machine Intelligence
WATCH NOW
WATCH NOW
Insurance - Risk Assessment
“Predictive analytics is the differentiator
for insurance companies going forward
in the next couple of decades.”
“Advanced analytics was one of the
key investments that we decided to
make.”
31

32. H2O.ai
Machine Intelligence
Fintech - Fraud/Risk/Churn/etc.
“H2O is a great solution because it's
designed to be enterprise ready and
can operate on very large datasets.”
”H2O has been a one-stop shop that helps
us do all our modeling in one framework.”
”H2O is the best solution to be able to
iterate very quickly on large datasets
and produce meaningful models.”
WATCH NOW
WATCH NOW
32
Today’s Keynote!

33. H2O.ai
Machine Intelligence 33
H2O Booklets
DOWNLOAD
Come get your booklets at our booth!
R Python Deep Learning GLMGBMSparkling Water

34. H2O.ai
Machine Intelligence 34
Data Scientists Love This Stuff
H2O GBM Model Tuning Tutorial for R/Python/Flow
https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/tutorials/gbm/

35. H2O.ai
Machine Intelligence 35
KDNuggets Poll about Deep Learning Tools & Platforms
http://www.kdnuggets.com
H2O and TensorFlow are tied
usage of Deep
Learning tools in
past year

36. H2O.ai
Machine Intelligence 36
TensorFlow + H2O + Apache Spark = Anything is possible
In progress: Integration with GPU DL tools (TensorFlow/Caffe/mxnet/etc.)
https://github.com/h2oai/sparkling-
water/blob/master/py/examples/
notebooks/TensorFlowDeepLearning.ipynb https://www.youtube.com/watch?v=62TFK641gG8

37. H2O
ALGORITHMS
EXPERIENCE
DATA
VERTICALS
• H2O Flow
Single web-based Document for
code execution, text, mathematics,
plots and rich media
• Visual Intelligence
UX and Interpretability for AI
• Steam
Elastic ML & Auto ML
Operationalize
Data Science
H2O.ai Now Focused On Experience
Beyond Algorithms and Data
37
DATA PRODUCTS

38. H2O.ai
Machine Intelligence 38
Steam - Automated Platform to Build and Scale Smart Data Products
DevOps/Data Engineers
Data Scientists
Advanced Data Scientists
Software Engineers
Application Software Engineers
DATA
BUSINESS
INSIGHTS
AI – Machine Learning Automation Scalability Visualization
Coming Soon

39. H2O.ai
Machine Intelligence 39
H2O OPEN TOUR w w w. O P E N . H 2 O . A I
We’re coming to a town near you in NYC / TX
Visit our Booth Today!

40. H2O.ai
Machine Intelligence
A.I. and Deep Learning are hot (again)!
Make your own smart data products with H2O!
Try H2O today - installs in minutes!
40
h2o.ai/download
https://www.youtube.com/user/0xdata/videos
https://github.com/h2oai/h2o-3
H2O Google Group
@h2oai
Summary
We’re hiring: h2o.ai/careers/