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SparkML: Easy ML Productization for Real-Time Bidding

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dataxu bids on ads in real-time on behalf of its customers at the rate of 3 million requests a second and trains on past bids to optimize for future bids. Our system trains thousands of advertiser-specific models and runs multi-terabyte datasets. In this presentation we will share the lessons learned from our transition towards a fully automated Spark-based machine learning system and how this has drastically reduced the time to get a research idea into production. We'll also share how we: - continually ship models to production - train models in an unattended fashion with auto-tuning capabilities - tune and overbooked cluster resources for maximum performance - ported our previous ML solution into Spark - evaluate the performance of high-rate bidding models Speakers: Maximo Gurmendez, Javier Buquet

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WIFI SSID:SparkAISummit | Password: UnifiedAnalytics
Maximo Gurmendez Javier Buquet SparkML: Easy ML Productization for Real- Time Bidding #UnifiedAnalytics #SparkAISummit
Boston Company Builds software for marketers to run effective programmatic marketing campaigns Automated decisioning at the core
bidder X bidder Y ad auction $3 $2 $1 Real Time Ad Bidding
dataxu: make marketing smarter through data science! Event data: Bids Wins Losses Attributions ML System Bidding models $3
Scale? Ø 2 Petabytes Processed Daily Ø 3 Million Bid Decisions Per Second Ø Runs 24 X 7 on 5 Continents Ø Thousands of ML Models Trained per Day
Goals of dataxu’s ML System Highly Predictive Fast to Bid (< 1 millisecond) Optimal use of training resources No downtime Always fresh models Unattended operation Self tuning Transparent Easy to deploy new algorithms
9 years ago Custom Hadoop Jobs (single pass) f(x) f(x) f(x) f(x) Campaign events training data Models used at bid time for each campaign
4 years ago: Can we use Spark? Thread safe? Is it fast enough? Does it use too much memory? Spark models work well with our data? Is it expensive to train? Can we use its out-the- box ML algorithms?
Problem #1: Data Partitioning 1 sample pass + 1 write pass beware of the fat reducers!
Problem #2: Spark models not ready for a low latency bidding setting Feature 1 Feature 2 1 0 1 1 0 1 Feature 1 Feature 2 Prediction 1 0 0.3 1 1 0.7 0 1 0.4 Spark Model Feature 1 Feature 2 1 0 Feature 1 Feature 2 Prediction 1 0 0.3 Model Needed At bid time things are different… Solution: Extended Spark with RowModels
Problem #2: Spark models not ready for a low latency bidding setting Solution: Extended Spark with RowModels
Problem #3: Categorical Features Encoding Slow F 1 F 2 A X A Y B Y StringIndexer F 1 F 2 IX 1 A X 0 A Y 0 B Y 1 StringIndexer F 1 F 2 IX 1 IX 2 A X 0 1 A Y 0 0 B Y 1 0 F 1 F 2 A X A Y B Y MultiTopK F 1 F 2 IX 1 IX 2 A X 0 1 A Y 0 0 B Y 1 0 Instead: Metwally, Agrawal, and Amr Abbadi (Efficient computation of frequent and top-k elements in data streams) Spark Typical:
Problem #4: Expensive to train We were running one campaign at a time… Observations: • Some campaigns took hours, some a few minutes • Some parts of training were IO bound, some CPU bound • We observed cluster idleness between jobs Solutions: • Launch in parallel smart batches of jobs • Carefully overbook the cluster resources, and not use “maxResourceAllocation” Result: 60% cheaper than legacy 1-pass Hadoop method!
Problem #5: How to switch systems? Decorated Spark Bidding Model Active Bidding Model A/B tests Spark model pulsed on that dayStage 1: Decorated Model
Problem #5: How to switch systems? Stage 2: Selected Bidding Machine Stage 3: Full Switch
Problem #5: How to switch systems? Everything went smoothly? Not exactly! • Reached S3 request limits upon deploy! • Rolled back • Implemented retries • Random waits • Back-offs & jitter • Latencies not exposed in simulations • Rolled back • Deeper profiling with YourKit
What about self-tuning, unattended operations? event data models calibrations Blackboard (S3) insights bidding manifests trainer model selector & calibrator insights builder manifest builder Bidding machines
What about transparency? { "model": { "partition": "Xm9ZgQEjav", "pipeline": "prospecting_random_forest", "uri": "s3://ml-bucket/../20180923.204250/" }, "bid_modifiers": [ { "name": "prospecting_random_forest", "parameters": { "profile": "quality_calibration" }, "type": "calibration", "uri": "s3://.../calibration.cjson" }, { "name": "insights-aware-bidding", "type": "insights-aware-bidding", "uri": ”s3://insights/../261716353" } ] }
Easy to add new algorithms? Took 2 days to port a standard Spark ML pipeline for a customer into production, thanks to the blackboard design.
DEMO 21#UnifiedAnalytics #SparkAISummit
Outcomes Benefits Lessons Greater flexibility to adapt to new use cases Better overall performance Better reliability and upgrade path 50% less code 60% savings Spark can be used for serious production systems Some tweaks are needed but still have the benefits of the 3rd Party ML libraries There’s no test like a full live test! Gradual switchover, pulsing and vigilance protected our business from harm.
DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT Thank You! mgurmendez@dataxu.com jbuquet@dataxu.com

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SparkML: Easy ML Productization for Real-Time Bidding

  • 1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics
  • 2. Maximo Gurmendez Javier Buquet SparkML: Easy ML Productization for Real- Time Bidding #UnifiedAnalytics #SparkAISummit
  • 3. Boston Company Builds software for marketers to run effective programmatic marketing campaigns Automated decisioning at the core
  • 4. bidder X bidder Y ad auction $3 $2 $1 Real Time Ad Bidding
  • 5. dataxu: make marketing smarter through data science! Event data: Bids Wins Losses Attributions ML System Bidding models $3
  • 6. Scale? Ø 2 Petabytes Processed Daily Ø 3 Million Bid Decisions Per Second Ø Runs 24 X 7 on 5 Continents Ø Thousands of ML Models Trained per Day
  • 7. Goals of dataxu’s ML System Highly Predictive Fast to Bid (< 1 millisecond) Optimal use of training resources No downtime Always fresh models Unattended operation Self tuning Transparent Easy to deploy new algorithms
  • 8. 9 years ago Custom Hadoop Jobs (single pass) f(x) f(x) f(x) f(x) Campaign events training data Models used at bid time for each campaign
  • 9. 4 years ago: Can we use Spark? Thread safe? Is it fast enough? Does it use too much memory? Spark models work well with our data? Is it expensive to train? Can we use its out-the- box ML algorithms?
  • 10. Problem #1: Data Partitioning 1 sample pass + 1 write pass beware of the fat reducers!
  • 11. Problem #2: Spark models not ready for a low latency bidding setting Feature 1 Feature 2 1 0 1 1 0 1 Feature 1 Feature 2 Prediction 1 0 0.3 1 1 0.7 0 1 0.4 Spark Model Feature 1 Feature 2 1 0 Feature 1 Feature 2 Prediction 1 0 0.3 Model Needed At bid time things are different… Solution: Extended Spark with RowModels
  • 12. Problem #2: Spark models not ready for a low latency bidding setting Solution: Extended Spark with RowModels
  • 13. Problem #3: Categorical Features Encoding Slow F 1 F 2 A X A Y B Y StringIndexer F 1 F 2 IX 1 A X 0 A Y 0 B Y 1 StringIndexer F 1 F 2 IX 1 IX 2 A X 0 1 A Y 0 0 B Y 1 0 F 1 F 2 A X A Y B Y MultiTopK F 1 F 2 IX 1 IX 2 A X 0 1 A Y 0 0 B Y 1 0 Instead: Metwally, Agrawal, and Amr Abbadi (Efficient computation of frequent and top-k elements in data streams) Spark Typical:
  • 14. Problem #4: Expensive to train We were running one campaign at a time… Observations: • Some campaigns took hours, some a few minutes • Some parts of training were IO bound, some CPU bound • We observed cluster idleness between jobs Solutions: • Launch in parallel smart batches of jobs • Carefully overbook the cluster resources, and not use “maxResourceAllocation” Result: 60% cheaper than legacy 1-pass Hadoop method!
  • 15. Problem #5: How to switch systems? Decorated Spark Bidding Model Active Bidding Model A/B tests Spark model pulsed on that dayStage 1: Decorated Model
  • 16. Problem #5: How to switch systems? Stage 2: Selected Bidding Machine Stage 3: Full Switch
  • 17. Problem #5: How to switch systems? Everything went smoothly? Not exactly! • Reached S3 request limits upon deploy! • Rolled back • Implemented retries • Random waits • Back-offs & jitter • Latencies not exposed in simulations • Rolled back • Deeper profiling with YourKit
  • 18. What about self-tuning, unattended operations? event data models calibrations Blackboard (S3) insights bidding manifests trainer model selector & calibrator insights builder manifest builder Bidding machines
  • 19. What about transparency? { "model": { "partition": "Xm9ZgQEjav", "pipeline": "prospecting_random_forest", "uri": "s3://ml-bucket/../20180923.204250/" }, "bid_modifiers": [ { "name": "prospecting_random_forest", "parameters": { "profile": "quality_calibration" }, "type": "calibration", "uri": "s3://.../calibration.cjson" }, { "name": "insights-aware-bidding", "type": "insights-aware-bidding", "uri": ”s3://insights/../261716353" } ] }
  • 20. Easy to add new algorithms? Took 2 days to port a standard Spark ML pipeline for a customer into production, thanks to the blackboard design.
  • 22. Outcomes Benefits Lessons Greater flexibility to adapt to new use cases Better overall performance Better reliability and upgrade path 50% less code 60% savings Spark can be used for serious production systems Some tweaks are needed but still have the benefits of the 3rd Party ML libraries There’s no test like a full live test! Gradual switchover, pulsing and vigilance protected our business from harm.
  • 23. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT Thank You! mgurmendez@dataxu.com jbuquet@dataxu.com