Machine Learning and Hadoop Present and Future Josh Wills Cloudera Data Science Team February 7th,...

Machine Learning and HadoopPresent and FutureJosh WillsCloudera Data Science TeamFebruary 7th, 2012

Today’s Speaker – Josh Wills

• [email protected]• Formerly of Google (2008 – 2011)

• Worked on the ad auction• Led the team that build the data infrastructure for Google+

• Before that: a bunch of startups• Sometimes as a software engineer, sometimes as a statistician

• Math degree from Duke and a half-finished PhD from The University of Texas at Austin

• Now: Director of Data Science at Cloudera

Copyright 2012 Cloudera Inc. All rights reserved

High Availability for Data Scientists


NIPS

Outline

• Part 1: Industrial Machine Learning• Part 2: Machine Learning and Hadoop

• State of the World• Where Things Are Headed

• Part 3: Offline/Online Batch/Real-Time


Industrial Machine Learning


Delta One: Model Evaluation

• Machine Learning is 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


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


Delta Three: Workflow


Practice Over Theory Blog

http://practiceovertheory.com/blog/2010/02/15/machine-learning-who-s-the-boss/

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


Outline





“Hadoop. It’s Where The Data Is.”


Hadoop Platform: Substrate

• Commodity servers• Open Compute

• Open source operating system• Linux

• Open source configuration management• Puppet• Chef

• Coordination service• ZooKeeper


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


Hadoop Platform: Integration

• Tool Access• FUSE• JDBC• ODBC

• Data Ingestion• Flume• Sqoop


ML and Hadoop: The State of the World


Computation: Plain Old MapReduce

• Great for:• Data Preparation• Feature Engineering• Model Validation/Evaluation

• Works For Certain Model Fitting Problems• Recommendation Systems• Expectation Maximization• Decision Trees (PLANET; Gradient Boosted Decision Trees)

• Not A Practical Option for Online Learning• Way More Detail from the KDD 2011 Talk


http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36296.pdf

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CDkQFjAC&url=http://lccc.eecs.berkeley.edu/papers.html&ei=D8PrTqK_KonJhAfJ6OypCA&usg=AFQjCNHKovKUzaNJMDIDtinhreVtu7U82g

http://www.slideshare.net/hadoop/modeling-with-hadoop-kdd2011

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)


https://github.com/cloudera/crunch

https://github.com/NICTA/scoobi

http://www.cascading.org/

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


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 is faster, has L-BFGS option

• Roll Your Own Instead: Naïve Bayes• Challenges

• “Secret sauce” effect• Delta between Mahout + the cutting edge in ML


More Machine Learning Interfaces for Hadoop

• Based on MapReduce• SystemML (IBM)

• R-Based Systems (Augment MapReduce with R)• Segue• RHIPE• RHadoop• Ricardo (IBM)


http://code.google.com/p/segue/

http://ml.stat.purdue.edu/rhipe/

https://github.com/RevolutionAnalytics/RHadoop

ML and Hadoop: Where Things are Headed


MRv2 and YARN

• Eliminates JobTracker bottleneck• Separate Resource Manager/Scheduler• Individual jobs have their own task masters• No more map slots and reduce slots

• Moves MapReduce into user-land• Hadoop clusters can run all sorts of jobs

• Will also allow fine-grained resource allocation• CPU• Memory• Disk


YARN Job Flows


The Contenders


AllReduce

• Developed at Yahoo! Research• Defines the allreduce operation

• N machines each have a number => each machine has the sum of the numbers

• At the heart of Vowpal Wabbit’s performance• Implemented in C++• Can be patched into Apache Hadoop and used today


http://hunch.net/?p=2094

Spark

• Developed at Berkeley’s AMP Lab

• Defines operations on distributed in-memory collections

• Written in Scala• Supports reading to and

writing from HDFS


http://www.spark-project.org/

GraphLab

• Developed at CMU• Lower-level primitives

• (but higher than MPI)

• Map/Reduce => Update/Sort

• Flexible, allows for asynchronous computations*

• C++/Java/Python/Matlab


http://graphlab.org/

Outline





Offline vs. Online Learning


Batch vs. Real-Time: The CAP Theorem

• Impossible for a distributed computer system to simultaneously provide:• Consistency• Availability• Partition Tolerance

• Instead, we end up with BASE• Basically Available Soft State Eventual consistency• High availability• Cleanup mechanism for providing consistency (eventually)


Nathan Marz: Beating the CAP Theorem


http://nathanmarz.com/blog/how-to-beat-the-cap-theorem.html

Models as Queries



Collapsing Distinctions



Systems Drive Algorithms, Redux



Questions?Want A [email protected]

Machine Learning and Hadoop Present and Future Josh Wills Cloudera Data Science Team February 7th,...

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