How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2.x with Richard Garris
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Richard Garris presented on ways to productionize machine learning models built with Apache Spark MLlib. He discussed serializing models using MLlib 2.X to save models for production use without reimplementation. This allows data scientists to build models in Python/R and deploy them directly for scoring. He also reviewed model scoring architectures and highlighted Databricks' private beta solution for deploying serialized Spark MLlib models for low latency scoring outside of Spark.
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2.x with Richard Garris
- 1. Richard Garris (Principal Solutions Architect) Apache Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
- 2. Empower anyone to innovate faster with big data. Founded by the creators of Apache Spark. Contributes 75% of the open source code, 10x more than any other company. VISION WHO WE ARE A fully-managed data processing platform for the enterprise, powered by Apache Spark. PRODUCT
- 3. CLUSTER TUNING & MANAGEMENT INTERACTIVE WORKSPACE PRODUCTION PIPELINE AUTOMATION OPTIMIZED DATA ACCESS DATABRICKSENTERPRISE SECURITY YOUR TEAMS Data Science Data Engineering Manyothers… BIAnalysts YOUR DATA Cloud Storage Data Warehouses Data Lake VIRTUAL ANALYTICSPLATFORM
- 4. About Me • Richard L Garris • rlgarris@databricks.com • Twitter @rlgarris • Principal Data Solutions Architect @ Databricks • 12+ years designing Enterprise Data Solutions for everyone from startups to Global 2000 • Prior Work ExperiencePwC, Google and Skytree – the Machine Learning Company • Ohio State Buckeye and Masters from CMU
- 5. Outline • Spark Mllib2.X • Model Serialization • Model Scoring SystemRequirements • Model Scoring Architectures • Databricks Model Scoring
- 6. About Apache Spark™ MLlib • Started with Spark 0.8 in the AMPLab in 2014 • Migration to Spark DataFrames started with Spark 1.3 with feature parity within 2.X • Contributions by 75+ orgs, ~250 individuals • Distributed algorithms that scale linearly with the data
- 7. MLlib’s Goals • General purpose machine learning library optimizedfor big data • Linearly scalable = 2x more machines , runtime theoretically cut in half • Fault tolerant = resilient to the failure of nodes • Covers the most common algorithms with distributed implementations • Built around the concept of a Data Science Pipeline (scikit-learn) • Written entirelyusing Apache Spark™ • Integrateswell withthe Agile Modeling Process
- 8. A Model is a MathematicalFunction • A model is a function: 𝑓 𝑥 • Linear regression 𝑦 = 𝑏0 + 𝑏1 𝑥1 + 𝑏2 𝑥2
- 10. ML Pipelines Train model 1 Evaluate Datasource 1 Datasource 2 Datasource 3 Extract featuresExtract features Feature transform 1 Feature transform 2 Feature transform 3 Train model 2 Ensemble A real pipeline!
- 11. ProductionizingModels Today Data Science Data Engineering Develop Prototype Model using Python/R Re-implement model for production (Java)
- 12. Problems with ProductionizingModels Develop Prototype Model using Python/R Re-implement model for production (Java) - Extra work - Different code paths - Data science does not translate to production - Slow to update models Data Science Data Engineering
- 13. MLlib 2.X Model Serialization Data Science Data Engineering Develop Prototype Model using Python/R Persist model or Pipeline: model.save(“s3n://...”) Load Pipeline (Scala/Java) Model.load(“s3n://…”) Deploy in production
- 14. Scala val lrModel = lrPipeline.fit(dataset) // Save the Model lrModel.write.save("/models/lr") • MLlib 2.X Model Serialization Snippet Python lrModel = lrPipeline.fit(dataset) # Save the Model lrModel.write.save("/models/lr") •
- 15. Model Serialization Output Code // List Contents of the Model Dir dbutils.fs.ls("/models/lr") • Output Remember this is a pipeline model and these are the stages!
- 16. TransformerStage (StringIndexer) Code // Cat the contents of the Metadata dir dbutils.fs.head(”/models/lr/stages/00_strI dx_bb9728f85745/metadata/part-00000") // Display the Parquet File in the Data dir display(spark.read.parquet(”/models/lr/sta ges/00_strIdx_bb9728f85745/data/")) Output { "class":"org.apache.spark.ml.feature.StringIndexerModel", "timestamp":1488120411719, "sparkVersion":"2.1.0", "uid":"strIdx_bb9728f85745", "paramMap":{ "outputCol":"workclassIdx", "inputCol":"workclass", "handleInvalid":"error" } } Metadata and params Data (Hashmap)
- 17. Estimator Stage (LogisticRegression) Code // Cat the contents of the Metadata dir dbutils.fs.head(”/models/lr/stages/18_logr eg_325fa760f925/metadata/part-00000") // Display the Parquet File in the Data dir display(spark.read.parquet("/models/lr/sta ges/18_logreg_325fa760f925/data/")) Output Model params Intercept + Coefficients {"class":"org.apache.spark.ml.classification.LogisticRegressionModel", "timestamp":1488120446324, "sparkVersion":"2.1.0", "uid":"logreg_325fa760f925", "paramMap":{ "predictionCol":"prediction", "standardization":true, "probabilityCol":"probability", "maxIter":100, "elasticNetParam":0.0, "family":"auto", "regParam":0.0, "threshold":0.5, "fitIntercept":true, "labelCol":"label” }}
- 18. Output Decision Tree Splits Estimator Stage (DecisionTree) Code // Display the Parquet File in the Data dir display(spark.read.parquet(”/models/dt/stages/18_dtc_3d614bcb3ff825/data/")) // Re-save as JSON spark.read.parquet("/models/dt/stages/18_dtc_3d614bcb3ff825/data/").json((”/models/json/dt").
- 20. What are the Requirementsfor a Robust Model Deployment System?
- 21. Model ScoringEnvironment Examples • In Web Applications / EcommercePortals • Mainframe / Batch ProcessingSystems • Real-TimeProcessingSystems/ Middleware • Via API / Microservice • Embeddedin Devices (Mobile Phones, Medical Devices,Autos)
- 22. Hidden Technical Debt in ML Systems “Hidden Technical Debt in Machine Learning Systems “, Google NIPS 2015 “Hidden Technical Debt in Machine Learning Systems “, Google NIPS 2015
- 25. Set Business Goals Understand Your Data Create Hypothesis Devise Experiment Prepare Data Train-Tune-Test Model Deploy Model Measure/Evaluate Results Deployment Should be Agile • Deploymentneeds to support A/B testing and experiments • Deploymentshould support measuring and evaluating model performance • Deploymentshould be fast and adaptive to business needs
- 26. Model A/B Testing, Monitoring, Updates • A/B testing – comparing two versions to see what performs better • Monitoring is the process of observing the model’s performance, logging it’s behavior and alerting when the model degrades • Logging should log exactly the data feed into the model at the time of scoring • Model update process • Benchmark (or Shadow Models) • Phase-In (20% traffic) • Avoid Big Bang
- 27. Consider the Scoring Environment Customer SLAs •Response time •Throughput (predictions per second) •Uptime / Reliability Tech Stack –C / C++ –Legacy (mainframe) –Java
- 28. Batch Real-Time Scoringin Batch vs Real-Time • Synchronous • Could be Seconds: – Customer is waiting (human real-time) • Subsecond: – High Frequency Trading – Fraud Detection on the Swipe • Asynchronous • Internal Use • Triggers can be event based on time based • Used for Email Campaigns, Notifications
- 29. Open Loop – human being involved Closed Loop – no human involved • Model Scoring – almost always closed loop, some open loop e.g. alert agents or customer service • Model Training – usually open loop with a data scientist in the loop to update the model Online Learning and Open / Closed Loop • Online is closed loop, entirely machine driven but modeling is risky • need to have proper model monitoring and safeguards to prevent abuse / sensitivity to noise • MLlib supports online through streaming models (k-means, logistic regression support online) • Alternative – use a more complex model to better fit new data rather than using online learning Open / ClosedLoop Online Learning
- 30. Model Scoring– Bot Detection Not All Models Return Boolean – e.g. a Yes / No Example: Login Bot Detector Different behavior depending on probability that use is a bot 0.0-0.4 ☞ Allow login 0.4-0.6 ☞ Send Challenge Question 0.6 to 0.75 ☞ Send SMS Code 0.75 to 0.9 ☞ Refer to Agent 0.9 - 1.0 ☞ Block
- 31. Model Scoring– Recommendations Output is a ranking of the top n items API – send user ID + number of items Returnsorted set of items to recommend Optional – pass contextsensitive informationto tailor results
- 33. Architecture Option A PrecomputePredictions using Spark and Serve fromDatabase Train ALS Model Send Email Offers to Customers Save Offers to NoSQL Ranked Offers Display Ranked Offers in Web / Mobile Recurring Batch
- 34. Architecture Option B Spark Streamand Score using an API with CachedPredictions Web Activity Logs Kill User’s Login SessionCompute Features Run Prediction Streaming Cache Predictions API Check
- 35. Architecture Option C Train with Spark and Score Outside of Spark Train Model in Spark Save Model to S3 / HDFS New Data Copy Model to Production Predictions Load coefficients and intercept from file
- 37. Databricks Model Scoring • Based on ArchitectureOption C • Goal: DeployMLlib model outside of Apache Spark and Databricks. • Easy to Embed in Existing Environments • Low Latency and Complexity • Low Overhead
- 38. • Train Model in Databricks – Call Fit on Pipeline – Save Model as JSON • Deploy model in external system – Add dependency on “dbml-local” package (without Spark) – Load model from JSON at startup – Make predictions in real time Databricks Model Scoring Code // Fit and Export the Model in Databricks val lrModel = lrPipeline.fit(dataset) ModelExporter.export(lrModel, " /models/db ") // In Your Application (Scala) import com.databricks.ml.local.ModelImport val lrModel= ModelImport.import("s3a:/...") val jsonInput = ... val jsonOutput= lrModel.transform(jsonInput)
- 39. Databricks Model ScoringPrivate Beta • Private BetaAvailable for Databricks Customers • Available on Databricks using Apache Spark 2.1 • Only logistic regressionavailable now • Additional Estimatorsand Transformersin Progress