Apache Mahout

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Requirements ● Easy administration. – Failover. – Monitoring. ● Easy to use. – Shield programmer from complexity. – Concentrate on functionality. – “Parallel programming on rails.”
We need a solution that: Is easy to use. Scales well beyond one node.
Page 1 and 2: Apache Mahout Large Scale Machine L
Page 3 and 4: Hello DAI!
Page 5 and 6: January 3, 2006 by Matt Callow http
Page 7 and 8: September 21, 2008, Rodrigo Galinde
Page 9 and 10: Machine learning - what's that?
Page 11 and 12: Archimedes model of nature
Page 14 and 15: An SVM's model of nature
Page 16 and 17: ● Industry ready. - Friendly lice
Page 18 and 19: January 8, 2008 by Pink Sherbet Pho
Page 20 and 21: Developers world wide
Page 22 and 23: Developers world wide Developers in
Page 24 and 25: Developers world wide Developers in
Page 26 and 27: Typical developer September 10, 200
Page 30: Java based implementation. Easy dis
Page 33 and 34: Contributions need not be Hadoop ba
Page 35 and 36: pattern=”http://[0-9A-Za-z\-_\.]*
Page 37 and 38: Assumptions: Data to process does n
Page 39 and 40: M A P | SHUFFLE | R E D U C E Local
Page 41 and 42: M A P | SHUFFLE | R E D U C E outpu
Page 43 and 44: private IntWritable one = new IntWr
Page 45 and 46: Options for running Hadoop ● Amaz
Page 47 and 48: Step 1: Serial implementation.
Page 49 and 50: Step 2: Parallelization strategies.
Page 51 and 52: Step 2: Parallelization strategies.
Page 53 and 54: Algorithm properties ● Job local
Page 55 and 56: Discover groups of items ● Group
Page 57: Going parallel: k-Means
Page 63 and 64: Until stable. Data intensive. Outpu
Page 65 and 66: Assign items to defined categories.
Page 67 and 68: Evolutionary algorithms ● Traveli
Page 69 and 70: Recommendation mining. ● ● ●
Page 71 and 72: What next? ● More algorithms. ●
Page 73 and 74: Reasons: Promotion, recruiting, and
Page 75 and 76: June 13, 2008, Star Guitar http://w
Page 77 and 78: Ted Dunning: “[...] if I have a c
Page 79 and 80:
Jumpstart your project with proven
Page 81 and 82:
Become part of the community.
Page 83 and 84:
Sept., 29 th 2009: Hadoop* Get Toge
Page 87 and 88:
Open questions. Gather data Large s
Page 89 and 90:
Algor. choice Gather data Extract f
Page 91 and 92:
Algor. choice Parameters Train mode
Page 93:
Algor. choice Parameters Train mode
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Apache Mahout

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