Multi Model Machine Learning by Maximo Gurmendez and Beth Logan
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Multi Model Machine Learning by Maximo Gurmendez and Beth Logan
- 1. Multi-Model Machine Learning for Real Time Bidding over Display Ads Beth Logan Senior Director of Optimization Maximo Gurmendez Data Science Engineering Team Lead With credit to our Spark developers: Inés Guelfi, Juan Tejería, Martin Manasliski, Victoria Seoane
- 2. We wanted to try Spark but wondered Thread safe? Is Spark fast enough? Does it use too much memory?
- 3. Agenda 1. What we do 2. How we do it 3. Why Spark? 4. Challenges Addressed 5. Main Takeaways
- 4. DataXu’s Mission Make marketing smarter through Data Science!
- 5. What We Do
- 6. Taking Action Automatically • Bid in real-time ad auctions on behalf of advertisers • Machine Learning System learns from past bids Browser Request Ad Ad exchanges Ad Selection + Bid Ad Bid Request DataXu Machine Learning system DataXu Real time system User
- 7. DataXu ML System Learn Models Ads shown User actions (purchase, clicks, etc) Only high quality models Hive database Calibrate Evaluate Real Time Bidding Hadoop
- 8. Why is this hard? Huge Scale • 2 Petabytes Processed Daily • 1.6 Million Bid Decisions Per Second • Runs 24 X 7 on 5 Continents • Thousands of ML Models Trained per Day Unattended Operation • Model training and deployment runs automatically every day Changing Industry • Need ability to adapt quickly to new customer requirements
- 9. Why Spark? • Large open source Machine Learning library – Fast turnaround of research to production – Easy to prototype and support new customer use cases – Built-in upgrade of algorithms – Increased reliability • Trains models faster than hadoop • Enables iterative models • Elastic environment via cloud
- 10. Challenges Addressed • Smart Dataset Partitioning by Campaign • Categorical Features • Functional Features • Pipelines + RowTransformers • Use of SparkSQL • Real-time model instantiations
- 11. Partitioning the data • Need 1 RDD per campaign • "Fat Reducers" or "Many files" problem • 2-pass solution
- 12. Partitioning the data: Solution • Sample the RDD • Construct histogram of sizes • Use histogram to allocate more processes (pseudo-sub- partition)
- 13. Spark ML Pipelines Raw Feature Transformation Feature Encoding Feature Selection Decision Tree Trainer Transformers Estimator
- 14. Spark ML Pipelines Transformer transform(Dataframe):Dataframe Model fit(Dataframe):Model Extends Estimator • Great for Training, evaluation & experimentation • Can we use them at bid time?
- 15. ML Pipelines: Row Transformer Problem: At bid time there is no DataFrame! Solution: Use row transformer Transformer transform(Dataframe):Dataframe RowTransformer transform(Row):Row Extends
- 16. Meta-Pipeline Extension • Combines and evaluates several pipelines • DAG with all steps and dependencies • JSON Configurable • Pipelines = All possible paths from root to leaves • Use to train multiple classifiers with little overhead (training time dominated by data read and transformation)
- 17. Best model varies from campaign to campaign AUC
- 18. Models at bid time • Standard java serialization • Models relatively light-weight and fast Add Preprocessing Metadata ~ 130K 0 20 40 60 80 100 120 140 Model Size in Memory (KB) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Current DataXu Model Spark Random Forest Avg. Latency (milliseconds)
- 19. • Choosing features via select command • Functional features and categorical to numerical encoding via UDFs • Top K feature values via UDAF • Reuse UDFs at bid time • Imperative to declarative • Huge savings in LOC Use and abuse of SparkSQL
- 20. SparkSQL:TopK UDAF Example For categorical encoding we first obtain most popular nominals: select topk(os) from training_data Result: {windows:1562, macos:928, linux:21}
- 21. SparkSQL: Feature Encoding Enumerate: select enumerate_encode(os), enumerate_encode(browser) from training_data One-Hot-Encoding: select onehot(os,’macos’), onehot(os,’windows’),onehot(os,’linux’) … Result: 1,3 3,1 2,1 Result: 1,0,0 0,1,0 0,0,1 Easily encode categorical features using UDFs
- 22. Takeaways •It works! • Spark SQL: maintainable & declarative • Models can bid at real-time • Automated & unattended ML at large scale • ML Pipelines had to be extended
- 23. THANK YOU! blogan@dataxu.com mgurmendez@dataxu.com dataxu.com/careers always looking for smart people!!