This document provides an overview of H2O.ai, an open-source in-memory predictive analytics platform. It was founded in 2011 and has 50+ core developers. H2O supports many machine learning algorithms like generalized linear models, random forest, gradient boosting, and deep learning. It can handle large datasets across various environments and programming interfaces like R, Python, and REST APIs. H2O provides scalable supervised and unsupervised learning algorithms for tasks like classification, regression, clustering, and dimensionality reduction.

1. H2O.ai
Machine Intelligence
Robust, Tested and Supported
Platform for Predictive Analytics

2. • Founded: 2011: Version 3 released in 2015
• Product: H2O open source in-memory predic=on engine
• Team: 50+ Core developers and data scien=sts
• HQ: Mountain View, CA. Sales: U.S., U.K. & Canada
H2O.ai Overview
H2O.ai
Machine Intelligence

4. What is H2O?
Open source in-memory predic=on engine PlaMorm
• Parallelized and distributed algorithms making the most use out of
mul=threaded systems and grids
• GLM, Random Forest, GBM, Deep Learning(ANN), GLRM, PCA, K-means etc.
Data, PlaMorm and Client Agnos=c Access
• Runs anywhere
• REST API– drives H2O from R, Python, Web UI, Excel, Tableau
• Score code for all models
Same code. Different Environments Scale
• Use all of your data without sub sebng
• No code changes to go from development to produc=on
Single source of truth for R and Python users

5. Ensemble
s
Deep Neural
Networks
Algorithms on H2O
• Generalized Linear Models with L1 and L2 Penalties:
Binomial, Gaussian, Gamma, Poisson and Tweedie
• Naïve Bayes
• Distributed Random Forest: Classification or regression models
• Gradient Boosting Machine: Produces an ensemble of
decision trees with increasing refined approximations
• Deep learning: Create multi-layer feed forward neural
networks starting with an input layer followed by multiple
layers of nonlinear transformations
Supervised Learning
Statistical
Analysis

6. Dimensionality
Reduction
Anomaly
Detection
Algorithms on H2O
• K-means: Partitions observations into k clusters/groups of the
same spatial size
• 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
• Autoencoders: Find outliers using a nonlinear dimensionality
reduction using deep learning
Unsupervised Learning
Clustering

7. Data and Client Agnos/c
HDFS
S3
SQL
NoSQL
Classifica=on
Regression
Feature
Engineering
In-Memory
Map Reduce/Fork Join
Columnar
Compression
Deep Learning
PCA, GLM, Cox
Random Forest /
GBM Ensembles
H2O Compute Engine
Streaming
Java Score Code
Matrix
Factoriza=on Clustering
Munging

8. H2O and R

9. Reading Data from Disk into H2O with R
STEP 1
R user
h2o_df = h2o.importFile(“Local/path/to/data.csv

10. Reading Data from Disk into H2O with R
Request data
from disk
STEP 2
HTTP REST
API request to
H2O
has local file
path
2.2
Initiate parallel
ingest
2.3
Disk 2.4
h2o.importFile()
2.1
R function
call
Local H2O
Instance
H2O
data.csv

11. Reading Data from Disk into H2O with R
STEP 3
h2o_df object
created in R
Disk
Local Host
Pointer to Data
Return pointer
to data in
REST API
JSON
Response
Disk
provides
data
3.3
3.4 3.1
data.csv
h2o_df
3.2
Parallelized
H2O
Frame in DKV
Local H2O
Instance
H2O
Frame
H2O