Machine Learning and Hadoop: Present and future
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Industrial machine learning, machine learning and Hadoop, and things industry needs from academia.
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Machine Learning and Hadoop: Present and future
- 1. Machine Learning and Hadoop Present and Future Josh Wills, Tom Pierce, and Jeff Hammerbacher Cloudera Data Science Team December 17th, 2011
- 2. High Availability for Data Scientists NIPS Copyright 2011 Cloudera Inc. All rights reserved
- 3. Agenda • Part 1: Industrial Machine Learning • Part 2: Machine Learning and Hadoop • State of the World • Where Things Are Headed • Part 3: Things Industry Needs From Academia Copyright 2011 Cloudera Inc. All rights reserved
- 4. Industrial Machine Learning Copyright 2011 Cloudera Inc. All rights reserved
- 5. Delta One: Model Evaluation • ML Systems Are One Piece of a Complex System • Well-defined objective functions are the exception • Multiple, often conflicting goals • Weights are fuzzy and shift with business priorities • Pareto optimization is the safest play • Predictive Accuracy Is Only Useful Up to a Point • Examples • Computational advertising • Friend recommendations on social networks Copyright 2011 Cloudera Inc. All rights reserved
- 6. Delta Two: Systems Precede Algorithms • Greenfield Projects Hardly Ever Happen • (and don’t usually launch) • Industrial Computational Infrastructure • General-purpose • Cheap • Shared • Constraints Drive Innovation • Vowpal Wabbit Hashing Trick • SETI @ Google Copyright 2011 Cloudera Inc. All rights reserved
- 7. Delta Three: Workflow Practice Over Theory Blog Copyright 2011 Cloudera Inc. All rights reserved
- 8. Delta Three: Workflow • Optimize the Overall Process • Model fitting is a small piece of the overall flow time • Parallelize everything • Better Features > Better Models • Fast Model Deployment • Common Feature Extraction Logic • Servable Models • Validation as Sanity Checking • Deploy to a small subset of real data and evaluate Copyright 2011 Cloudera Inc. All rights reserved
- 9. Agenda • Part 1: Industrial Machine Learning • Part 2: Machine Learning and Hadoop • State of the World • Where Things Are Headed • Part 3: Things Industry Needs From Academia Copyright 2011 Cloudera Inc. All rights reserved
- 10. Hadoop: It’s Where The Data Is Copyright 2011 Cloudera Inc. All rights reserved
- 11. Hadoop Platform: Substrate • Commodity servers • Open Compute • Open source operating system • Linux • Open source configuration management • Puppet • Chef • Coordination service • ZooKeeper Copyright 2011 Cloudera Inc. All rights reserved
- 12. Hadoop Platform: Storage • Distributed schema-less storage • HDFS • Ceph • Append-only storage formats and metadata • Avro • RCFile • HCatalog • Mutable key-value storage and metadata • HBase Copyright 2011 Cloudera Inc. All rights reserved
- 13. Hadoop Platform: Integration • Tool Access • FUSE • JDBC • ODBC • Data Ingestion • Flume • Sqoop Copyright 2011 Cloudera Inc. All rights reserved
- 14. ML and Hadoop: The State of the World Copyright 2011 Cloudera Inc. All rights reserved
- 15. Computation: Plain Old MapReduce • Great for: • Data Preparation • Feature Engineering • Model Validation/Evaluation • Works For Certain Model Fitting Problems • Recommendation Systems • Decision Trees (PLANET; Gradient Boosted Decision Trees) • Not A Practical Option for Online Learning • Way More Detail from the KDD 2011 Talk Copyright 2011 Cloudera Inc. All rights reserved
- 16. Tools for Data Preparation/Feature Engineering • Languages/Environments • PigLatin • HiveQL • Need to deal with mismatch between offline/online feature generation • Java/Scala APIs • Crunch (Cloudera) • Scoobi (NICTA) • Cascading (Concurrent) • Jaql (IBM) Copyright 2011 Cloudera Inc. All rights reserved
- 17. Apache Mahout • The starting place for MapReduce-based machine learning algorithms • Not machine-learning-in-a-box • Custom tweaks/modifications are the rule • A disparate collection of algorithms for: • Recommendations • Clustering • Classification • Frequent Itemset Mining Copyright 2011 Cloudera Inc. All rights reserved
- 18. Apache Mahout (cont.) • Best Library: Taste Recommender • Oldest project, most widely-deployed in production • SVD implementation is particularly active • Good Libraries: Online SGD • Does not use MapReduce • Vowpal Rabbit + AllReduce is faster, has L-BFGS option • Roll Your Own Instead: Naïve Bayes • Challenges • “Secret sauce” effect • Delta between Mahout + the cutting edge in ML Copyright 2011 Cloudera Inc. All rights reserved
- 19. More Machine Learning Interfaces for Hadoop • Based on MapReduce • SystemML (IBM) • AllReduce (Vowpal Wabbit) • No MapReduce • Spark • R-Based Systems (Augment MapReduce with R) • Segue • RHIPE • RHadoop • Ricardo (IBM) Copyright 2011 Cloudera Inc. All rights reserved
- 20. ML and Hadoop: Where Things are Headed Copyright 2011 Cloudera Inc. All rights reserved
- 21. MRv2 and YARN • Eliminates JobTracker bottleneck • Separate Resource Manager/Scheduler • Individual jobs have their own task masters • Moves MapReduce into user-land • Enables Hadoop clusters to run all sorts of jobs • MPI (Hamster; MAPREDUCE-2911) • Native BSP (Giraph) • Spark • AllReduce, GraphLab Copyright 2011 Cloudera Inc. All rights reserved
- 22. Agenda • Part 1: Industrial Machine Learning • Part 2: Machine Learning and Hadoop • State of the World • Where Things Are Headed • Part 3: Things Industry Needs From Academia Copyright 2011 Cloudera Inc. All rights reserved
- 23. Machine Learning on Multivariate Time Series • 1e5 writes/sec • Positive events are relatively rare • Feature extraction challenge • May not be clear what the right time horizon is • Tight SLAs • Very high stakes Copyright 2011 Cloudera Inc. All rights reserved
- 24. An Academic Language For Feature Engineering • Feature extraction/selection is as important as model fitting • e.g., hierarchical feature representation, impact on training time and experiment design, feature cost modeling, etc. • Academic literature on this problem is sparse and dispersed across multiple fields • NIPS 2003 • HCI, NLP, Information Retrieval, etc. • We need a common language for talking about these problems across disciplines Copyright 2011 Cloudera Inc. All rights reserved
- 25. A Broader Ontology For Model Selection • Practical factors that enter into the “best” choice of model… • Data arrival rate • Data volume • Scoring latency • Model refresh time • Robustness/reliability • …in addition to the standard predictive power/simplicity tradeoffs Copyright 2011 Cloudera Inc. All rights reserved
- 26. Questions? Want A Job? @josh_wills