Spark Summit East Talk

1. Spark and the Future of
Advanced Analytics
Thomas W. Dinsmore
Consultant and Author

2. Do we need
a distributed platform
for machine learning?

3. Arguments Against

4. 1

5. 1

6. Small
datasets
Large datasets
(> 1TB)
1

7. 64
66
68
70
72
74
76
78
80
10,000 100,000 1,000,000 10,000,000
AUC
Sample Size
Model Accuracy
Source: http://datascience.la/benchmarking-random-forest-im plementations/
2

8. 3

9. 4
Titan A450
4x16=64 Cores
1TB RAM
~$20K

10. 5

11. Arguments For

12. 1

13. Data
Wrangling
Feature
Engineering
Model
Training
Prediction
• Structure
• Select
• Sample
• Aggregate
• Transform
• Score

14. Enterprise Data
Data
Wrangling
Feature
Engineering
Model
Training
Scoring

15. 2

16. 3

17. Source: http://datascience.la/benchmarking-random-forest-im plementations/
64
66
68
70
72
74
76
78
80
10,000 100,000 1,000,000 10,000,000
AUC (*)
Sample Size
Model Accuracy
Linear Random Forests
4
Source: http://datascience.la/benchmarking-random-forest-im plementations/
(*) HoldoutSample

18. 5

19. vs.

20. 1 GPU: CNTK is a little faster
0
2,000
4,000
6,000
8,000
10,000
12,000
1 GPU 1 x 4 GPUs 2 x 4 GPUs
Speed
Deep LearningBenchmark
CNTK TensorFlow

21. 4 GPUs: CNTK is a lot faster.
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
1 GPU 1 x 4 GPUs 2 x 4 GPUs
Speed
Deep LearningBenchmark
CNTK TensorFlow

22. 0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
1 GPU 1 x 4 GPUs 2 x 4 GPUs
Speed
Deep LearningBenchmark
CNTK TensorFlow
2x4 GPUs: TensorFlow can’t.

23. The future of analytics is distributed.
• Your data sources and targets are distributed.
– You may only need a snippet of data
– You still have to retrieve that snippet
• Data movement is expensive.
• Data requirements are expanding.
• Machine learning algorithms can use more data.
• When you need capability, you’d better have it.

25. Distribution Framework
Distribution Framework
Distribution Framework
Distribution Framework
Distribution Framework

26. Open Source
Tool
Distribution Framework
Open Source
Tool
Open Source
Tool

28. Data
Wrangling
Feature
Engineering
Model
Training
Scoring

30. Data Loader for Hadoop
SPSS Analytic Server

32. • Most functions push down to Spark
• Can embed PySpark, SparkR scripts

34. • Graphical interface to Spark MLlib
• Limited data manipulation functions
• Scoring interface through PMML

35. Sparkling Water package:
• Enables H2O to work with Spark RDDs, DataFrames
• Publish Spark data structures as H2O Frames

36. Data
Profiling

37. Data
Profiling
Feature
Engineering
Model
Training

38. Data
Profiling
Feature
Engineering
Model
Training
Leaderboard

39. Data
Profiling
Feature
Engineering
Model
Training
Model
Selection
Leaderboard

40. Data
Profiling
Model
Deployment
Feature
Engineering
Model
Training
Model
Selection

41. Summary
• Yes, distributed machine learning is necessary.
• Need generalized distribution framework.
• Today, Spark is the only game in town.
• Race is on to deliver push-down Spark integration.

42. THANK YOU.
Thomas W. Dinsmore
@thomaswdinsmore
The Big Analytics Blog
www.thomaswdinsmore.com