Machine Learning, Deep Learning - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

1. ML + H2O
AlexTellez & Michal Malohlava
www.h2o.ai
lib .ai

2. THE RED PILL (SPARK + ML)
Finally, ONE TO RULE THEM ALL!
1. Scrape & Collect Data
2. Cleanse Data + Feature Extraction / Engineering
3. Build Machine Learning Models + Iterate
4. Throw More Data to Improve Model
5. Deploy Model(s) in Real-Time

3. THE BLUE PILL (H2O.AI)
What is H2O? (water, duh!)
It is ALSO an open-source, distributed and parallel predictive
engine for machine learning.
What makes H2O different?
Cutting-edge algorithms + parallel architecture + ease-of-use
=
Happy Data Scientists / Analysts

4. WHY NOT BOTH PILLS?!
Build smarter applications USING BOTH in harmony within
the Spark Ecosystem !!!
Convert Spark RDDs H2O RDDs for Machine Learning

5. LET’S BUILD AN APP!
Task: Predict the job category from
a Craigslist AdTitle

6. ML WORKFLOW
1. Perform Feature Extraction on Words + Munging
2. Run Word2Vec algo (MLlib) on JobTitle words
3. Create “title vectors” from
individual word vectors for each job title
4. Pass the Spark RDD H2O RDD for ML in Flow
5. Run H2O GBM algorithm on H2O RDD
6. Create Spark Streaming Application + Score on new data

7. 1.TEXT MUNGING
Example: “Site Supervisor and Pre K Teachers Needed Now!!!”
Post Tokenization: Seq(site, supervisor, pre, teachers, needed)
val tokens = jobTitles.map(line => token(line))
Next: Apply Spark’s Word2Vec model to each word

8. 2.WORD2VEC
Simply: A mathematical way to represent a single word as a vector of
numbers. These vector ‘representations’ encode information about the
about a given word (i.e. its meaning)
Post Tokenization: Seq(site, supervisor, pre, teachers, needed)
Post Word2Vec Results:
needed, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]
site, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]
supervisor, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]

9. BUTTHAT’S ON WORDS!
Post Word2Vec Results:
needed, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]
site, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]
supervisor, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]
WE NEED TITLE VECTORS BASED ON ALL THE WORDS!
HOW?
Averaging word vectors to make ‘TitleVectors’
v(King) - v(Man) +V(Woman) ~ v(Queen)

10. 3.TITLEVECTORS
In Steps:
1. Sum the word2vec vectors in a given title
2. Divide this sum by # of words in a given title
Result: ~ Average vector for a given title of N words
needed, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]
site, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]
supervisor, mllib.linalg.vector[0.456, 0.123, 0.678…….0.987]+
+
Divide by Total Words (post tokenization)
~ (site supervisor….needed), [0.998, 0.349, 0.621…….0.915]

11. 4. PASS SPARK RDDTO H2O
OPEN H2O FLOW!

12. 5. BUILD A MODEL!

13. 80% ACCURACY - DEFAULT!
Algo: Gradient Boosting Machine
#Trees: 50
# Bins: 20
Depth: 5
(ALL DEFAULTVALUES)
~ 20% Error Rate

14. 6. SPARK STREAMING +
DEPLOYMENT
Create Spark Streaming App to read in new Job Titles
a) Create a Spark Streaming Producer - Reads data from a file &
generates events in real-time which we will predict category.

15. APP ARCHITECTURE
Posting
job title
“HIRING
Painting
CONTRACTORS
NOW!!!”
Stream
Categorize
a job title
Prediction = “Labor”
Re-train
the model
Craigslist jobs
Word2Vec
Model
GBM
Model
Word2Vec
Train a model

16. “ASK CRAIG” LIVE DEMO!

17. END-TO-END
In JUST 25 minutes…we:
1. Performed sophisticated feature extraction + engineering
2. Passed a Spark RDD H2O RDD for ML
3. Created a Spark Stream to read in new data
5. “Productionalized” H2O + Spark MLlib model to score on new data
So happy I took
both pills!
4. Built a GBM to classify titles w/ 80% accuracy

18. TRY SPARKLING WATER!!
Download @ h2o.ai
Coming Soon: Release 1.4 for Spark 1.4!
NEW GUI! H2O FLOW
Meetup: SiliconValley Big Data Science