H2O is an open-source machine learning platform designed for scalable and efficient predictive analytics on big data, providing robust support for model productionalization in enterprise environments. The document details H2O's architecture, key components, supported algorithms, and installation steps, while also discussing model deployment options and benchmarking results for various algorithms on different frameworks. It highlights the capabilities and performance of H2O in training large datasets, specifically related to predicting flight delays.

1. AI Development With

2. Introduction
H2O is an open source, in-memory,
distributed, fast, and scalable machine
learning and predictive analytics platform
Build machine learning models on big data and
provides easy productionalization of those
models in an enterprise environment.

3. H2O Scientific Council
Dr. Trevor Hastie
• PhD in Statistics, Stanford
University
• John A. Overdeck Professor of
Mathematics, Stanford University
• Co-author, The Elements of
Statistical Learning: Prediction,
Inference and Data Mining
• Co-author with John Chambers,
Statistical Models in S
• Co-author, Generalized Additive
Models
• 108,404 citations (via Google
Scholar)
Dr. Rob Tibshirani
• PhD in Statistics, Stanford
University
• Professor of Statistics and Health
Research and Policy, Stanford
University
• COPPS Presidents’ Award recipient
• Co-author, The Elements of
Statistical Learning: Prediction,
Inference and Data Mining
• Author, Regression Shrinkage and
Selection via the Lasso
• Co-author, An Introduction to the
Bootstrap
Dr. Stephen Boyd
• PhD in Electrical Engineering and
Computer Science, UC Berkeley
• Professor of Electrical Engineering
and Computer Science, Stanford
University
• Co-author, Convex Optimization
• Co-author, Linear Matrix
Inequalities in System and Control
Theory
• Co-author, Distributed
Optimization and Statistical
Learning via the Alternating
Direction
• Method of Multipliers

4. H2O Architecture

5. H2O Supported Algorithms
Statistical Analysis
• Linear Models (GLM)
• Cox Proportional Hazards
• Naïve Bayes
Ensembles
• Random Forest
• Distributed Trees
• Gradient Boosting Machine
• R Package - Super Learner
Ensembles
Deep Neural Networks
• Multi-layer Feed-Forward Neural
Network
• Auto-encoder
• Anomaly Detection
• Deep Features
Clustering
• K-Means
Dimension Reduction
• Principal Component Analysis
• Generalized Low Rank Models
Optimization
• Generalized ADMM Solver
• L-BFGS (Quasi Newton Method)
• Ordinary Least-Square Solver
• Stochastic Gradient Descent
Data Munging
• Integrated R-Environment
• Slice, Log Transform

6. H2O Components
• Multi-node cluster with shared memory model.
• All computations in memory.
• Each node sees only some rows of the data.
• No limit on cluster size.
• Objects in the H2O cluster such as data frames, models
and results are all referenced by key.
• Any node in the cluster can access any object in the
cluster by key.
• Distributed data frames (collection of vectors).
• Columns are distributed (across nodes) arrays.
• Each node must be able to see the entire dataset (achieved
using HDFS, S3, or multiple copies of the data if it is a CSV
file).

7. Installing H2O
• Click the Download H2O button on the
http://h2o-release.s3.amazonaws.com/h2o/latest_stable.html
page.
• From your terminal, run:
• Point your browser to http://localhost:54321

8. Starting H2O and Loading Data (Python)
import pandas.io.sql as pandas_sql
from h2o import h2o
h2o.init()
connection = connect(host=settings.DB_HOST,
user=settings.DB_USER,
password=settings.DB_PASS,
db=settings.DB_NAME,
charset='utf8mb4',
cursorclass=cursors.DictCursor)
my_pandas_data_frame = pandas_sql.read_sql_query('select * from my_table', connection)
my_h2o_data_frame = h2o.H2OFrame(my_pandas_data_frame)

9. Data Manipulation (Python)
from h2o import h2o
# Importing Data
training_data_frame = h2o.import_file(path=‘myDataFile’)
# Merge the first dataset into the second dataset.
df3 = df2.merge(df1)
# Use group_by with multiple columns. Summarize the destination, arrival delays, and departure delays for
an origin
cols_1 = ['Origin', 'Dest', 'IsArrDelayed', 'IsDepDelayed']
cols_2 = ["Dest", "IsArrDelayed", "IsDepDelayed"]
air[cols_1].group_by(by='Origin').sum(cols_2, na ="ignore").get_frame()
# Split the data into Train/Test/Validation with Train having 70% and test and validation 15% each
train,test,valid = training_data_frame .split_frame(ratios=(.7, .15))

10. Cross Validation (Python)
from h2o import h2o
random_forest_model = h2o.H2ORandomForestEstimator(
model_id=”MyModelId",
ntrees=20,
max_depth=10,
min_rows=4,
nfolds=10,
seed=12345
)
random_forest_model.train(x=feature_columns,
y='label',
training_frame=my_data_frame)

11. Model Deployment
H2O allows you to convert the models you have
built to either a Plain Old Java Object (POJO) or
a Model Object, Optimized (MOJO).
H2O-generated MOJO and POJO models are
intended to be easily embeddable in any Java
environment

12. H2O Model POJO

13. H2O Model POJO
import hex.genmodel.GenModel;
import hex.genmodel.easy.EasyPredictModelWrapper;
@Bean
public EasyPredictModelWrapper listenModeModel() {
try {
Class<?> clazz = Class.forName("com.frauctive.model.FrauctiveRandomForestModel");
GenModel rawModel = (hex.genmodel.GenModel) clazz.newInstance();
return new EasyPredictModelWrapper(
new EasyPredictModelWrapper.Config()
.setModel(rawModel)
.setConvertUnknownCategoricalLevelsToNa(true)
.setConvertInvalidNumbersToNa(true)
);
} catch (Exception e) {
throw new FrauctiveException(e.getMessage(), e);
}
}

14. H2O Model POJO Prediction
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.prediction.BinomialModelPrediction;
public static void main(String[] args) throws Exception {
hex.genmodel.GenModel rawModel;
rawModel = (hex.genmodel.GenModel) Class.forName("gbm_pojo_test";).newInstance();
EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel);
RowData row = new RowData();
row.put("Year", "1987");
row.put("Month", "10");
row.put("DayofMonth", "14");
BinomialModelPrediction p = model.predictBinomial(row);
System.out.println("Label (aka prediction) is flight departure delayed: " + p.label);
}

15. H2O MOJO
At large scale, new models are roughly 20-25
times smaller in disk space
2-3 times faster during "hot" scoring, and 10-
40 times faster in "cold" scoring compared to
POJOs.
The efficiency gains are larger the bigger the
size of the model.

16. H2O Model MOJO (R)
library(h2o)
h2o.init(nthreads=-1) path = system.file("extdata", "prostate.csv", package="h2o")
h2o_df = h2o.importFile(path)
h2o_df$RACE = as.factor(h2o_df$RACE)
model = h2o.gbm(y="CAPSULE",
x=c("AGE", "RACE", "PSA", "GLEASON"),
training_frame=h2o_df,
distribution="bernoulli",
ntrees=100,
max_depth=4)
modelfile = model.download_mojo(path="~/experiment/", get_genmodel_jar=True)
print("Model saved to " + modelfile) Model saved to /Users/user/GBM_model_R_1475248925871_74.zip"

17. H2O Model MOJO Prediction (Java)
public static void main(String[] args) throws Exception {
EasyPredictModelWrapper model = new
EasyPredictModelWrapper(MojoModel.load("GBM_model_R_1475248925871_74.zip"));
RowData row = new RowData();
row.put("AGE", "68");
row.put("RACE", "2");
row.put("DCAPS", "2");
row.put("VOL", "0");
row.put("GLEASON", "6");
BinomialModelPrediction p = model.predictBinomial(row);
}

18. Benchmarking Analysis - Algorithms
The algorithms studied are
• Linear (logistic regression, linear SVM)
• Random Forest
• Boosting
• Deep Neural Network

19. Benchmarking Analysis - Frameworks
Commonly used open source implementations
• R packages
• Python scikit-learn
• Vowpal Wabbit
• H2O
• xgboost
• Spark MLlib.

20. Benchmarking Analysis - Data
Training datasets of sizes 10K, 100K, 1M,
10M are generated from the well-known
airline dataset (using years 2005 and 2006).
A test set of size 100K is generated from the
same (using year 2007).
The task is to predict whether a flight will be
delayed by more than 15 minutes.

21. Results - Linear Models
Tool n Time (sec) RAM (GB) AUC
R 10K 0.1 1 66.7
. 100K 0.5 1 70.3
. 1M 5 1 71.1
. 10M 90 5 71.1
Python 10K 0.2 2 67.6
. 100K 2 3 70.6
. 1M 25 12 71.1
. 10M crash/360 71.1
VW 10K 0.3 (/10) 66.6
. 100K 3 (/10) 70.3
. 1M 10 (/10) 71.0
. 10M 15 71.0
H2O 10K 1 1 69.6
. 100K 1 1 70.3
. 1M 2 2 70.8
. 10M 5 3 71.0
Spark 10K 1 1 66.6
. 100K 2 1 70.2
. 1M 5 2 70.9
. 10M 35 10 70.9

22. Results - Random Forest
Tool n Time (sec) RAM (GB) AUC
R 10K 50 10 68.2
. 100K 1200 35 71.2
. 1M crash
Python 10K 2 2 68.4
. 100K 50 5 71.4
. 1M 900 20 73.2
. 10M crash
H2O 10K 15 2 69.8
. 100K 150 4 72.5
. 1M 600 5 75.5
. 10M 4000 25 77.8
Spark 10K 50 10 69.1
. 100K 270 30 71.3
. 1M crash/2000 71.4
xgboost 10K 4 1 69.9
. 100K 20 1 73.2
. 1M 170 2 75.3
. 10M 3000 9 76.3

23. Results – Big(ger) Data
Tool Time[s] RAM[GB]
R 1000 60
Spark 160 120
H2O 40 20
VW 150
Linear models, 100M rows:
Tool Time[s] RAM[GB]
H2O 500 100
VW 1400
Linear models, 1B rows:
Algo Tool Time[s] Time[hr]
RAM[G
B]
RF H2O 40000 11 80
. xgboost 36000 10 60
GBM H2O 35000 10 100
. xgboost 110000 30 50
RF/GBM, 100M rows:

24. Demo

25. References
• https://www.stat.berkeley.edu/~ledell/docs/h2o_hpccon_oct20
15.pdf
• https://github.com/szilard/benchm-ml
• http://docs.h2o.ai/h2o/latest-stable/h2o-docs/
• https://www.slideshare.net/0xdata/rf-brighttalk
• https://github.com/h2oai/h2o-3/blob/master/h2o-
docs/src/product/howto/MOJO_QuickStart.md

26. Thanks