H2O is an open source machine learning platform that provides interfaces for R, Python, and Scala. It allows for data exploration, model building, and feature generation on large datasets using high performance machine learning algorithms. The document discusses how to get started with H2O in R, including downloading and reading documentation, exploring data, building models, and exporting models for real-time scoring. It also provides overviews of the H2O architecture and how algorithms are run in a distributed manner across an H2O cluster from R.

1. H2O.ai
Machine Intelligence
Intro to R & H2O
By: Spencer Aiello

2. Agenda
1. short, short history of R
2. what is h2o
3. getting h2o and reading documentation
4. data exploration
5. model building

3. Getting H2O & Docs
1. http://h2o.ai/download/
a. Bleeding Edge (link)
b. Install in R (tab)
1. build h2o (https://github.com/h2oai/h2o-3#4-building-h2o-3)
1. http://docs.h2o.ai/ -> H2O 3.0 -> R Users (link) -> R docs (link)

4. H2O.ai
Machine Intelligence
A Brief History of R:
- R first appears 22 years ago (1993)*
- Implementation of S (which was created by John Chambers @ Bell Labs)
* Python first appeared 24 years ago (1991)

5. H2O.ai
Machine Intelligence
H2O is what exactly?
Services:
- Interfaces to mainstream data science languages (R, Python, Scala)
- I/O common data formats (CSV, zipped, HDFS, ORC, parquet!?)
- Interface with modern big data infrastructures: Hadoop, Spark, H2O
- Feature-generation capabilities
- High Performance State-of-the-Art Machine Learning Algorithms

6. H2O.ai
Machine Intelligence
H2O is what exactly?
Object Taxonomy in H2O
- H2OFrame: A 2D collection of uniformly typed columns
- H2OModel: An H2O model object
- ID/Key: An identifier for an H2O object

7. H2O.ai
Machine Intelligence
H2O is what exactly?
Feature Generation Capabilities
- > 100 operations to perform on an H2OFrame
- Aggregations:
- mean, min, max, sum, or any user-defined reduction
- distributed parallel group-by
- table, cut
- Simple String manipulation: trim, sub, gsub
- Date Formatting/Extraction: get/set timezones, month, year, dayOfWeek
- Transformations: sqrt, log, *,+, …
- Filtering: R-like slicing

8. H2O.ai
Machine Intelligence
H2O is what exactly?
H2O Modeling

9. H2O.ai
Machine Intelligence
H2O is what exactly?
Infrastructure for:
- KFold Cross-Validation
- Grid Search
- Model Import/Export

10. H2O.ai
Machine Intelligence
H2O is what exactly?
Export Models For Real-Time Scoring:

11. H2O.ai
Machine Intelligence
25,000 commits /
3yrs
H2O World Conference 2014
Team@H2O.ai

12. H2O.ai
Machine Intelligence
Driving H2O From R
H2O
H2O
H2O
data.csv
HTTP REST API
request to H2O
H2O ClusterInitiate
distributed ingest
Some Data
Location
Request
data
STEP 2
2.2
2.3
2.4
R
h2o.importFile()
2.1
R function call

13. H2O.ai
Machine Intelligence
Driving H2O From R
H2O
H2O
H2O
R
Some data
location
STEP 3
Cluster IP
Cluster Port
Pointer to Data
Return pointer
to data in REST
API JSON
Response
data provided
3.3
3.4
3.1h2o_df object
created in R
data.csv
h2o_df
H2O
Frame
3.2
Distributed H2O
Frame in DKV
H2O Cluster

14. H2O.ai
Machine Intelligence
R Script Starting H2O GLM
HTTP
REST/JSON
.h2o.startModelJob()
POST /3/ModelBuilders/glm
h2o.glm()
R script
Standard R process
TCP/IP
HTTP
REST/JSON
/3/ModelBuilders/glm endpoint
Job
GLM algorithm
GLM tasks
Fork/Join
framework
K/V store
framework
H2O process
Network layer
REST layer
H2O - algos
H2O - core
User process
H2O process
Legend

15. H2O.ai
Machine Intelligence
R Script Retrieving H2O GLM Result
HTTP
REST/JSON
h2o.getModel()
GET /3/Models/glm_model_id
h2o.glm()
R script
Standard R process
TCP/IP
HTTP
REST/JSON
/3/Models endpoint
Fork/Join
framework
K/V store
framework
H2O process
Network layer
REST layer
H2O - algos
H2O - core
User process
H2O process
Legend