This talk was held at the 10th meeting on February 3rd 2014 by Sean Owen. Having collected Big Data, organizations are now keen on data science and “Big Learning”. Much of the focus has been on data science as exploratory analytics: offline, in the lab. However, building from that a production-ready large-scale operational analytics system remains a difficult and ad-hoc endeavor, especially when real-time answers are required. Design patterns for effective implementations are emerging, which take advantage of relaxed assumptions, adopt a new tiered "lambda" architecture, and pick the right scale-friendly algorithms to succeed. Drawing on experience from customer problems and the open source Oryx project at Cloudera, this session will provide examples of operational analytics projects in the field, and present a reference architecture and algorithm design choices for a successful implementation.

1. Design Patterns for Large-Scale
Real-Time Learning
Sean Owen / Director of Data Science / Cloudera
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2. What We Talk About When
We Talk About Data Science
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3. www.quora.com/Data-Science/What-is-the-difference-between-a-data-scientist-and-a-statistician
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4. 4

5. tist
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6. Data Science Is Exploratory Analytics?
www.tc.umn.edu/~zief0002/Comparing-Groups/blog.html
thenextweb.com/microsoft/2013/07/08/microsoft-brings-the-office-store-to-22-new-markets-adds-power-bi-an-intelligence-tool-to-office-365/
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7. 7

8. Example:
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Search, ML over Patient Data
MapReduce for indexing, learning
HBase for storage and fast access
Also: Storm for
incremental update
And: relational DB for
most recent derived data
API façade for input;
API for querying learning
Engineering
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Machine Learning
engineering.cerner.com/2013/02/near-real-time-processing-over-hadoop-and-hbase/

9. Adding Operational Analytics
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10. 2014: Lab to Factory
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11. Data Science Will Be Operational Analytics
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12. I Built A Model. Now What?
Collect Input
Repeat
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Build Model
Query Model

13. I Built A Model On Hadoop. Now What?
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Collect Input
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Repeat
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Build Model
?
Query Model

14. Example: Oryx
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15. www.mwttl.com/wp-content/uploads/2013/11/IMG_5446_edited-2_mwttl.jpg
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16. cloudera/ml
+
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17. Gaps to fill, and Goals
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Model Building
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Model Serving
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Large-scale
Continuous
Apache Hadoop™-based
Few, good algorithms
Real-time query
Real-time update
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Algorithms
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Parallelizable
Updateable
Works on diverse input
Interoperable
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PMML model format
Simple REST API
Open source

18. Large-Scale or Real-Time?
Large-Scale
Offline
Batch
vs
Real-Time
Online
Streaming
Why Don’t We Have Both?
λ!
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19. Lambda Architecture
Batch, Stream
Processing are different
• Tackle separately in
2+ Layers
• Batch Layer: offline,
asynchronous
• Serving / Speed Layer:
real-time, incremental,
approximate
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… λ?
jameskinley.tumblr.com/post/37398560534/the-lambda-architecture-principles-for-architecting
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20. Batch
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Serving/Speed

21. Two Layers
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Computation Layer
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Java-based server process
Client of Hadoop 2.x
Periodically builds
“generation” from recent
data and past model
Baby-sits MapReduce*
jobs (or, locally in-core)
Publishes models
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Serving Layer
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* Apache Spark later
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Apache Tomcat™-based
server process
Consumes models from
HDFS (or local FS)
Serves queries from
model in memory
Updates from new input
Also writes input to HDFS
Replicas for scale

22. Collaborative Filtering : ALS
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Alternating Least Squares
Latent-factor model
Accepts implicit or
explicit feedback
Real-time update
via fold-in of input
No cold-start
Parallelizable
YT
X

23. Clustering : k-means++
Well-known and
understood
• Parallelizable
• Clusters updateable
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cwiki.apache.org/confluence/display/MAHOUT/K-Means+Clustering
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24. Classification / Regression : RDF
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Random Decision Forests
Ensemble method
Numeric, categorical
features and target
Very parallel
Nodes updateable
Works well on many
problems
age$ 30
>$
female?
income$ 20000
>$
Yes
Yes
Yes
No

25. PMML
Predictive Modeling
Markup Language
• XML-based format for
predictive models
• Standardized by Data
Mining Group
(www.dmg.org)
• Wide tool support
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<PMML xmlns="http://www.dmg.org/PMML-4_1"
version="4.1">
<Header copyright="www.dmg.org"/>
<DataDictionary numberOfFields="5">
<DataField name="temperature"
optype="continuous"
dataType="double"/>
…
</DataDictionary>
<TreeModel modelName="golfing"
functionName="classification">
<MiningSchema>
<MiningField name="temperature"/>
…
</MiningSchema>
<Node score="will play">
<Node score="will play">
<SimplePredicate field="outlook"
operator="equal"
value="sunny"/>
…
</Node>
</Node>
</TreeModel>
</PMML>
www.dmg.org/v4-1/TreeModel.html
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26. HTTP REST API
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Convention for RPC-like
request / response
HTTP verbs, transport
GET : query
POST : add input
Easy from browser, CLI,
Java, Python, Scala, etc.
GET /recommend/jwills
HTTP/1.1 200 OK
Content-Type: text/plain
"Ray LaMontagne",0.951
"Fleet Foxes",0.7905
"The National",0.688
"Shearwater",0.3017

27. Wish List
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Revamp workflow
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De-emphasize model
building
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Well-solved
Bring your own
Emphasize integration
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Oozie?
Spark / Crunch-like API,
not raw M/R
PMML, etc.
More component-ized
• Less black-box service
• More “push” options
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Flume?
“Pull” options
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Kafka?
Hive / Impala ?

28. Open Source
github.com/cloudera/oryx
100% Apache License 2.0
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