This document provides an agenda and overview for a workshop on H2O-3 and Sparkling Water. The agenda includes introductions to H2O-3 and Sparkling Water, hands-on sessions for data cleaning, supervised learning, feature engineering and time series, and a question and answer period. The overview explains what H2O is, how it works as an in-memory AI engine, its distributed algorithms and easy to use APIs. It also describes the H2O cluster architecture and hands-on session using a Lending Club loan default dataset.

1. H2O-3 and Sparkling Water
Workshop
Megan Kurka / Tom Kraljevic
H2O.ai

2. Agenda
• Introduction to H2O-3
• Hands On
• Data Cleaning and Supervised Learning
• Introduction to Sparkling Water
• Hands On
• Feature Engineering for Time Series and AutoML
• Questions and Answers

3. H2O Overview

4. H2O Products

5. What is Machine Learning?

6. X
y
Regression:
How much will a customer spend?
Classification:
Will a customer churn?
xi
xj
yes
no
H2O algos:
Penalized Linear Models
Random Forest
Gradient Boosting
XGBoost
Neural Networks
Stacked Ensembles
H2O algos:
Penalized Linear Models
Naïve Bayes
Random Forest
Gradient Boosting
XGBoost
Neural Networks
Stacked Ensembles
Supervised Learning

7. Clustering:
Grouping rows – e.g. creating groups
of similar customers
H2O algos:
k – means
xi
xj
Feature extraction:
Grouping columns – Create a small
number of new representative
dimensions
xi
xj
PC1 = -0.3 xi - 0.4 xi
H2O algos:
Principal components
Generalized low rank models
Autoencoders
Word2Vec
xi
xj
Fraudster
Billionaire
Anomaly detection:
Detecting outlying rows - Finding
high-value, fraudulent, or
weird customers
H2O algos:
Principal components
Generalized low rank models
Autoencoders
Unsupervised Learning

8. Simplified Typical Machine Learning Pipeline
Model Model
Building
Features
Target
Modeling
Table
Data Quality
& Transformation
Data
Integration
+

9. H2O-3

10. What is H2O?
Open source in-memory AI engine Math Platform
• Parallelized and distributed algorithms
• GLM, Random Forest, GBM, Deep Learning, etc.
Easy to use and adopt Tech and API
• Written in Java – perfect for Java Programmers
• Install is lightweight
• REST API (Java) – run H2O from R, Python, WebUI
More data? Or better models? BOTH Big Data
• Use all of your data – model without sampling
• More Data + Better Models = Better Predictions

11. H2O Cluster
H2O

12. H2O Clients
H 2 O C l u s t e r
H2
O
H2O

13. H 2 O C l u s t e r
REST
/
JSON
Local
Machine
H2
O
H2O

14. H 2 O C l u s t e r
REST
/
JSON
Local
Machine
H2
O
H2O

15. H 2 O C l u s t e r
REST
/
JSON
Local
Machine
H2
O

16. JVM 1
JVM 2
JVM N
REST
/
JSON
Local
Machine
H2
O

17. JVM 1
JVM 2
JVM N
H 2 O C l u s t e r
REST
/
JSON
Local
Machine
H2
O

18. Local
Machine
H2
O
JVM 1
JVM 2
JVM N
H 2 O C l u s t e r
x1
x2
x3
…
xp
y
REST
/
JSON

19. cluster returns
frame key
Local
Machine
H2
O
JVM 1
JVM 2
JVM N
H 2 O C l u s t e r
x1
x2
x3
…
xp
y
REST
/
JSON

20. H2O-3 Hands On

21. The Data
• Lending Club peer-to-peer loan default dataset
• Covers loans issued in years 2007-2011
• 42,538 loans
Type of information Column names
Demographic Information annual_inc, home_ownership, emp_length
Loan Information purpose, term, desc, int_rate
Response Column bad_loan