Sri Ambati – CEO, 0xdata at MLconf ATL
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"Comparing Variable Importance from Ensemble and Deep Learning Methods for AdTech Data" Variable Importance brings interpretability to popular black box modeling techniques. In this talk we study performance of popular ensemble techniques like Random Forest, Gradient Boosting with GLM. We observe certain traits that get magnified by non-linear techniques like Deep Learning that are otherwise missed by GBM or Random Forest. We describe Open Source Scalable Machine Learning package, H2O which through ease-of-use and speed makes comparisons and picking best-of-breed and ensembles more natural. H2O's implementation of these algorithms tracks popular open source and text book implementations closely.
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Sri Ambati – CEO, 0xdata at MLconf ATL
- 1. H2O.ai Open Source Machine Learning for Intelligent Applications H2O.ai Machine Intelligence
- 2. Time is the only non-renewable resource Speed Matters! H2O.ai Machine Intelligence
- 3. Sampling Law of Large Numbers
- 4. On Premise On / Off Hadoop On EC2 Per Node 2M Row ingest/sec 50M Row Regression/sec 750M Row Aggregates / sec Tableau R JSON Scala Java Python H2O Prediction Engine SDK / API Nano Fast Scoring Engine Deep learning Regression Trees Boosting Forests Solvers Gradients ensembles Cluster Query Processor R-engine In-Mem Map Reduce Distributed fork/join Memory Manager Columnar Compression Classify HDFS S3 SQL NoSQL Excel H2O.ai Machine Intelligence
- 5. Infrastructure Parallelism Data Parallel Chunking Express! Algorithm Parallel Parallel Code blocks Math Parallelism ADMM, HogWild Distribution Zero-Serialization – endian wars have ended
- 6. Scalable Machine Learning For Smarter Applications H2O.ai Machine Intelligence H2O.ai
- 7. Programmable Internet H2O.ai Machine Intelligence
- 8. Programmable Devices H2O.ai Machine Intelligence
- 9. AdSense Sense H2O.ai Machine Intelligence
- 10. Correlation Causality H2O.ai Machine Intelligence
- 11. Data Sensors Devices Events. Signals. TimeSeries Semi-structured data. json. High velocity. High dimensions. H2O.ai Machine Intelligence
- 12. Streaming Data Historical Data Scoring from prediction Anomaly and Outliers Detection Unsupervised Learning H2O.ai Machine Intelligence
- 13. Streaming Data Historical Data Anomaly and Outliers Detection model Scoring from prediction H2O.ai Machine Intelligence
- 14. Streaming Data Historical Data Clustering / Unsupervise Learning model Scoring from prediction H2O.ai Machine Intelligence
- 15. H2O.ai Machine Intelligence https://developer.nest.com/documentation/api-reference/devices
- 16. Take Models to Production in Java H2O.ai Machine Intelligence
- 17. Onset of Rita H2O.ai Machine Intelligence
- 18. Common ensemble techniques Bayesian Classifiers Ensembles of all hypotheses in hypothesis-space. Bagging Each model votes with equal weight. Bagging trains models on randomly drawn subset Boosting Incrementally build an ensemble of each new model H2O.ai Machine Intelligence
- 21. Gradient Boosting Machine H2O.ai Machine Intelligence
- 24. Variable Importance Comparison Gradient Boosting Machine, 50 trees Random Forest, 50 trees H2O.ai Machine Intelligence
- 25. Generalized Linear Modeling – Variable Importance GLM, Elastic Net (Binomial) GLM, Elastic Net (Binomial) Categorical expansion on Age H2O.ai Machine Intelligence
- 26. Variable Importance Comparison Deep Learning (Tanh / 4-layer) Deep Learning (Tanh / 3-layer) H2O.ai Machine Intelligence
- 27. every generation needs to invent it’s math. Our data, our tools! H2O.ai Machine Intelligence
- 28. Power-Law
- 29. Code is incomplete without Community! Open Source Matters! H2O.ai Machine Intelligence
- 31. Community Committers 30 Meet ups 90 in 12 months Coverage Conference Speakers Curriculum Stanford, MIT, CSU, SUNY, SJSU, Purdue
- 32. Data Driven Decision Making is hard! Courage Matters! H2O.ai Machine Intelligence
- 33. Thanks Courtney, Nick & MLConf for bringing us to ATL
- 34. Sparkling Water Application Life Cycle Sparkling App jar file Spark Master JVM spark-submit Spark Worker JVM Spark Worker JVM Spark Worker JVM (1) (2) (3) (1) User submits App to Spark cluster Master node (2) App distributed to Spark cluster Worker nodes (3) Spark Executor JVMs start for App (4) H2O instance starts within each Executor JVM (5) App’s Scala main program runs Sparkling Water Cluster Spark Executor JVM H2O (4) Spark Executor JVM H2O Spark Executor JVM H2O
- 35. Sparkling Water Data Distribution Sparkling Water Cluster H2O H2O H2O Spark Executor JVM Data Source (e.g. HDFS) (1) (2) (3) (1) Use Spark SQL to read data into a Spark RDD (2) Convert Spark RDD to H2O RDD; H2O RDD is column-based and highly compressed (Not shown) Run modeling and prediction workflows with H2O (3) Convert H2O RDD (e.g. predictions) back to Spark RDD H2O RDD Spark RDD Spark Executor JVM Spark Executor JVM
- 36. H2O HHDFS H2O YARN HHDFS H2O Hadoop MR HHDFS Standalone YARN H2O in MR H HortonWorks, Cloudera, MapR, Intel 2O.ai Machine Intelligence
- 37. H2O – The Killer-App for Spark MLlib H2O SQL H2ORDD HDFS=DATA Sparkling Water H2O.ai Machine Intelligence In-Memory Big Data, Columnar ML 100x faster Algos R CRAN, API, fast engine API Spark API, Java MM Community Devs, Data Science
- 38. examples H2O.ai Machine Intelligence
- 40. Fraud / No-fraud 1/1000 unbalanced Click-Stream Browse / Click / Buy H2O.ai Machine Intelligence
- 41. Propensity Models Merchants –to- Users Lifetime Value of Customer Pricing Engines H2O.ai Machine Intelligence