Metis Presentation May 2016
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Transcript of Metis Presentation May 2016
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ML Little Data
Vincent TangLead ML Engineer
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SAMSUNG ACCELERATOR
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EMBEDDED ML
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BIG DATA, BIG COMPUTE
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STANDARD DATA PIPELINE + LEARNING
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DEVICES IN THE WILD
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Move Compute ML to the Data Edge
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MOVE ML TO THE EDGE
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Traditional Embedded
Resources MOAR GPUs Each thread counts; small buffers
Power 60-130 watts / server 0.18 mW for 32 bytes/second
Updates Commit + Push OTA (sometimes)
Languages Python & R FTW! C, C++, Java
Parameters Stationarity Non-stationarity
Cycle Batch Online, up to 1600hz
Type Supervised Unsupervised
Variance “Napolean Dynamite” problem Unreliable sensors
Metric arg max (accuracy) arg max (accuracy / big-O)
COMPARISON
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PIPELINE
Acquisition (20%) Feature Engineering (60%) Learning (10%) Deploy (10%)
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PIPELINE
Acquisition (20%) Feature Engineering (60%) Learning (10%) Deploy (10%)
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PIPELINE
Acquisition (20%) Feature Engineering (60%) Learning (10%) Deploy (10%)
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PIPELINE
Acquisition (20%) Feature Engineering (60%) Learning (10%) Deploy (10%)
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DEEP NETS
Acquisition (20%) Feature Engineering (60%) Learning (10%) Deploy (10%)
Feature Engineering & Learning for the price of one!
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PIPELINE
Acquisition (20%) Feature Engineering (60%) Learning (10%) Deploy (10%)
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PIPELINE
Acquisition (20%) Feature Engineering (60%) Learning (10%) Deploy (10%)
Tighter Feedback & Cleaner Code!
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● More data > smarter algorithm● Start with simple learners, then increase complexity as needed● Cast a wide net, then prune● Reject hypotheses early and often
ADVICE FOR PRACTITIONERS
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SAMSUNG
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CASE STUDY: UNCLIP
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Thank you!