Release Spark ML Pipelines in Production with MLeap
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MLeap is a tool that allows machine learning models trained using Spark ML to be deployed to production environments without Spark. It addresses common issues like data scientists and engineers having to re-write data pipelines and model code for production. MLeap uses Spark for training but removes the Spark dependency for deployment. It provides core machine learning components, a runtime for transformations, and serialization to bundle models. This allows models to be deployed to APIs and services more quickly than traditional Spark-based approaches. Benchmarks show MLeap models can transform data over 20x faster than equivalent Spark models.
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Release Spark ML Pipelines in Production with MLeap
- 1. MLeap: Release Spark ML Pipelines Hollin Wilkins & Mikhail Semeniuk
- 2. Introduction Studied some Math and Economics @ University of Minnesota - Predictive modeling of at risk patients @ UHG - R/SAS batch pipelines -- Joins TrueCar to do new car price modeling - SQL-based batch pipelines + python for Linear Regressions in API layer - Continues to train models in SAS/R - SQL-based batch pipelines + python for Linear Regressions in API layer - Massive pre- computation in Hadoop/ElasticSearch - Portions of models still translated, now to Java - Spark enables data processing and training of models in same environment MLeap Web Dev @ Cornell Studied some General Biology Rails Consulting for TrueCar and other companies Implement ML model for ClearBook in Ruby, Python and Java Erlang for highly- concurrent game servers C/C# Game Engines Join TrueCar for Rails/mobile development Begin work on machine learning platform based on Spark/Hadoop How do we build real-time APIs based on Spark Models? SparkContext prohibitive Try several ideas, fail, learn a ton, begin MLeap project
- 3. Everyone wants to do better! The winning technology will be the one that enables Engineers and Data Scientists to collaborate and work across a single platform. Action Reaction Problem Statement: Deploying machine learning algorithms to a production environment is a lot more difficult than it has to be and is a common source of friction at data-driven organizations - Data scientists write data pipelines to construct research datasets - Engineers re-write the data pipelines for a production-ready system - Engineers write scalable libraries for computing features and algorithms - Data scientists largely don’t use those libraries and maintain/re-write their own copy of the code - Data scientists largely focus on linear/logistic regressions due to engineering constraints - Talented engineers get largely tired of coding up linear regressions and updating coefficients
- 4. Existing Solutions: You won’t believe how many companies are still deploying algorithms in a SQL environment! And these are billion dollar operations. Hard-Coded Models (SQL, Java, Ruby) PMML Emerging Solutions (yHat, DataRobot) Enterprise Solutions (Microsoft, IBM, SAS) MLeap Quick to Implement Open Sourced Committed to Spark/Hadoop API Server Infrastructure Lesson Learned: Push code down to where the data is, not the other way around!
- 5. MLeap Solution • Born out of need to deploy models quickly to a real time API server • Leverage Hadoop/Spark ecosystem for training, get rid of Spark dependency for execution • Easily reuse models with serialization and executing without Spark
- 6. MLeap Components • core - provides linear algebra system, regression models, and feature builders • runtime - provides DataFrame-like “LeapFrame” and transformers for it • spark - provides easy conversion from Spark transformers to MLeap transformers • serialization - common serialization format for Spark and MLeap (Bundle.ML) New features: expanded serialization formats to include both json and protobuf for large models (i.e. random forests with thousands of features) mleap-spark mleap-runtime mleap-core Bundle.ML mleap-serialization
- 7. Linear Algebra Dense/Sparse Vectors BLAS from Spark Feature Builders VectorAssembler StringIndexer Regressions LinearRegression RandomForest MLeap Core Components Classifiers RandomForest LogisticRegression Gradient Boosted Reg. Trees StandardScaler mleap-core
- 8. MLeap Runtime • Provides LeapFrame, which stores data for transformations by MLeap transformers • MLeap transformers use mleap-core building blocks to transform LeapFrame • MLeap transformers correspond one-to-one with Spark transformers • No dependencies on Spark mleap-runtime
- 9. Feature Pipeline VectorAssembler Continuous Feature Vector StandardScaler StringIndexer StringIndexer StringIndexer OneHotEncoder OneHotEncoder VectorAssembler LinearRegression Categorical Feature Categorical Feature Index Categorical Feature One Hot Vector Categorical Feature Vector VectorAssembler Scaled Continuous Feature Vector Final Feature Vector Continuous Feature Legend Final Feature Vector Prediction Regression Pipeline OneHotEncoder
- 10. Categorical Pipeline LeapFrame LeapFrame LeapFrame Categorical Feature StringIndexer OneHotEncoder Categorical Feature Index Categorical Feature One Hot Vector StringIndexer OneHotEncoder
- 11. MLeap Serialization (Bundle.ML) • Provides common serialization for both Spark and MLeap • 100% protobuf/JSON based for easy reading, compact data, and portability • No dependencies on Parquet * • Can be written to zip files, file system, HDFS, anywhere with an FS-like structure mleap-serialization
- 12. Linear Regression Model String Indexer Model Linear Regression Model (Code)
- 13. MLeap Spark • Train an ML pipeline with Spark then export it to MLeap • Execute an MLeap pipeline against a Spark DataFrame Spark Estimator Spark Model MLeap Model MLeap Spark Spark DataFrame Spark LeapFrame Spark LeapFrame MLeap Spark Spark DataFrame MLeap Transformer MLeap Spark
- 14. Benchmarks MLeap: 0.011ms/transform Spark: 23.4ms/transform
- 15. MLeap API + Server Algo 1 Algo 2 Algo n Spark + Hadoop + HDFS MLlib Scikit + other MLeap Transformers + Serialization Web Services Java API REST API Mobile Apps Spark Jobs Map/ Reduce Pipeline Code Notebooks
- 16. Demo Usage of MLeap • Train a sample listing price model using linear regression and random forest against some AirBnb training data • Deploy both models to an API server • Get real-time results • IN UNDER 5 MINUTES!
- 17. Future of MLeap • Unify linear algebra and core libraries with Spark • Python/R interface • Deploy easily to embedded systems and outside of JVM • Full support for all Spark transformers
- 18. MLeap Development • Currently 5 people working on projects across 4 different companies • Talk to us if you are interested in deploying this technology at your company • MLeap Demo Project: https://github. com/TrueCar/mleap-demo
- 19. Thank You! Hollin Wilkins email: hollinrwilkins@gmail.com github: https://github.com/hollinwilkins twitter: https://twitter.com/HollinWilkins Mikhail Semeniuk email: seme0021@gmail.com github: https://github.com/seme0021 twitter: https://twitter.com/MikhailSemeniuk