Machine Learning and HadoopPresent and FutureJosh Wills, Tom Pierce, and Jeff HammerbacherCloudera Data Science TeamDecemb...
High Availability for Data Scientists                 NIPS                Copyright 2011 Cloudera Inc. All rights reserved
Agenda• Part 1: Industrial Machine Learning• Part 2: Machine Learning and Hadoop  • State of the World  • Where Things Are...
Industrial Machine Learning   Copyright 2011 Cloudera Inc. All rights reserved
Delta One: Model Evaluation• ML Systems Are One Piece of a Complex System• Well-defined objective functions are the except...
Delta Two: Systems Precede Algorithms• Greenfield Projects Hardly Ever Happen   • (and don’t usually launch)• Industrial C...
Delta Three: Workflow                                                                 Practice Over Theory Blog           ...
Delta Three: Workflow• Optimize the Overall Process   • Model fitting is a small piece of the overall flow time   • Parall...
Agenda• Part 1: Industrial Machine Learning• Part 2: Machine Learning and Hadoop  • State of the World  • Where Things Are...
Hadoop: It’s Where The Data Is    Copyright 2011 Cloudera Inc. All rights reserved
Hadoop Platform: Substrate• Commodity servers   • Open Compute• Open source operating system   • Linux• Open source config...
Hadoop Platform: Storage• Distributed schema-less storage   • HDFS   • Ceph• Append-only storage formats and metadata   • ...
Hadoop Platform: Integration• Tool Access   • FUSE   • JDBC   • ODBC• Data Ingestion   • Flume   • Sqoop                  ...
ML and Hadoop: The State of the World        Copyright 2011 Cloudera Inc. All rights reserved
Computation: Plain Old MapReduce• Great for:   • Data Preparation   • Feature Engineering   • Model Validation/Evaluation•...
Tools for Data Preparation/Feature Engineering• Languages/Environments   • PigLatin   • HiveQL   • Need to deal with misma...
Apache Mahout• The starting place for MapReduce-based machine  learning algorithms   • Not machine-learning-in-a-box   • C...
Apache Mahout (cont.)• Best Library: Taste Recommender   • Oldest project, most widely-deployed in production   • SVD impl...
More Machine Learning Interfaces for Hadoop• Based on MapReduce  • SystemML (IBM)  • AllReduce (Vowpal Wabbit)• No MapRedu...
ML and Hadoop: Where Things are Headed          Copyright 2011 Cloudera Inc. All rights reserved
MRv2 and YARN• Eliminates JobTracker bottleneck   • Separate Resource Manager/Scheduler   • Individual jobs have their own...
Agenda• Part 1: Industrial Machine Learning• Part 2: Machine Learning and Hadoop  • State of the World  • Where Things Are...
Machine Learning on Multivariate Time Series • 1e5 writes/sec • Positive events are   relatively rare • Feature extraction...
An Academic Language For Feature Engineering• Feature extraction/selection is as important as model  fitting   • e.g., hie...
A Broader Ontology For Model Selection• Practical factors that enter into the “best” choice of  model…   •   Data arrival ...
Questions?Want A Job?  @josh_wills
Upcoming SlideShare
Loading in...5
×

Hadoop and Machine Learning

17,798

Published on

Slides for the talk by the Cloudera Data Science team on the state of machine learning and Hadoop at NIPS 2011.

Published in: Technology, Education
0 Comments
41 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
17,798
On Slideshare
0
From Embeds
0
Number of Embeds
11
Actions
Shares
0
Downloads
436
Comments
0
Likes
41
Embeds 0
No embeds

No notes for slide

Transcript of "Hadoop and Machine Learning"

  1. 1. Machine Learning and HadoopPresent and FutureJosh Wills, Tom Pierce, and Jeff HammerbacherCloudera Data Science TeamDecember 17th, 2011
  2. 2. High Availability for Data Scientists NIPS Copyright 2011 Cloudera Inc. All rights reserved
  3. 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. 4. Industrial Machine Learning Copyright 2011 Cloudera Inc. All rights reserved
  5. 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. 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. 7. Delta Three: Workflow Practice Over Theory Blog Copyright 2011 Cloudera Inc. All rights reserved
  8. 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. 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. 10. Hadoop: It’s Where The Data Is Copyright 2011 Cloudera Inc. All rights reserved
  11. 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. 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. 13. Hadoop Platform: Integration• Tool Access • FUSE • JDBC • ODBC• Data Ingestion • Flume • Sqoop Copyright 2011 Cloudera Inc. All rights reserved
  14. 14. ML and Hadoop: The State of the World Copyright 2011 Cloudera Inc. All rights reserved
  15. 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. 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. 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. 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. 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. 20. ML and Hadoop: Where Things are Headed Copyright 2011 Cloudera Inc. All rights reserved
  21. 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. 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. 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. 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. 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. 26. Questions?Want A Job? @josh_wills
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×