RS Barga STRATA'18 New York City
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Presentation on continuous learning over streaming data, presented at Strata'18 in NYC.
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RS Barga STRATA'18 New York City
- 1. Continuous Machine Learning over Streaming Data Roger Barga, GM Amazon Web Services Nina Mishra and Sudipto Guha, Principal Scientist(s) AWS Kapil Chhabra, Rubrik Inc The Story Continues…
- 2. All data originates in real-time!
- 3. { "payerId": "Joe", "productCode": "AmazonS3", "clientProductCode": "AmazonS3", "usageType": "Bandwidth", "operation": "PUT", "value": "22490", "timestamp": "1216674828" } Metering Record 127.0.0.1 user-identifier frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326 Common Log Entry <165>1 2003-10-11T22:14:15.003Z mymachine.example.com evntslog - ID47 [exampleSDID@32473 iut="3" eventSource="Application" eventID="1011"][examplePriority@32473 class="high"] Syslog Entry “SeattlePublicWater/Kinesis/123/Realtime” – 412309129140 MQTT Record <R,AMZN,T,G,R1> NASDAQ OMX Record Smart Buildings Beacons Smart Textiles Health Monitors
- 4. But, analytics to gain insights is usually done much, much later.
- 6. Batch analytics operations take too long BusinessValue Time To Action Data originated Analytics performed Insights gleaned Action taken Outdated insights Impotent or harmful actions PositiveNegative Decision made Poor decision
- 7. Compress the analytics lifecycle Maximize the value of data BusinessValue Time To Action PositiveNegative Maximum Value
- 8. #Streaming
- 9. Streaming technology is necessary to detect and act on real-time perishable insights.
- 10. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Kinesis Data Streaming Services Get actionable insights quickly Streaming Ingest data as it’s generated Process data on the fly Real-time analytics/ML, alerts, actions
- 11. Robust Random Cut Forrest Summary of a dynamic data stream, highly efficient, wide number of use cases…
- 13. Random Cut Tree Recurse: The cutting stops when each point is isolated. Range-biased Cut 0 10 5
- 14. Random Cut Forest Each tree built on a random sample. …
- 15. Random Sample of a Stream Reservoir Sampling [Vitter] Maintain random sample of 5 points in a stream? Keep heads with probability Discard tails with probability 5 6 1 6
- 16. Insert – Case I Start with the Root If the point falls inside the bounding box follow the path to the appropriate child
- 17. Insert – Case II Theorem: Insert generates a tree T’ ~ T( )
- 18. What is an Outlier?
- 19. Anomaly Score: Displacement A point is an anomaly if its insertion greatly increases the tree size ( = sum of path lengths from root to leaves = description length). Inlier:
- 21. NYC Taxi Ridership 0 5000 10000 15000 20000 25000 30000 2014-12-0100:00:00 2014-12-0104:00:00 2014-12-0108:00:00 2014-12-0112:00:00 2014-12-0116:00:00 2014-12-0120:00:00 2014-12-0200:00:00 2014-12-0204:00:00 2014-12-0208:00:00 2014-12-0212:00:00 2014-12-0216:00:00 2014-12-0220:00:00 2014-12-0300:00:00 2014-12-0304:00:00 2014-12-0308:00:00 2014-12-0312:00:00 2014-12-0316:00:00 2014-12-0320:00:00 2014-12-0400:00:00 2014-12-0404:00:00 2014-12-0408:00:00 2014-12-0412:00:00 2014-12-0416:00:00 2014-12-0420:00:00 2014-12-0500:00:00 2014-12-0504:00:00 2014-12-0508:00:00 2014-12-0512:00:00 2014-12-0516:00:00 2014-12-0520:00:00 2014-12-0600:00:00 2014-12-0604:00:00 2014-12-0608:00:00 2014-12-0612:00:00 2014-12-0616:00:00 2014-12-0620:00:00 2014-12-0700:00:00 2014-12-0704:00:00 2014-12-0708:00:00 2014-12-0712:00:00 2014-12-0716:00:00 2014-12-0720:00:00 numPassengers Mon 8am 6pm 4pm Sat 11pm 11am Tue Wed Thu Fri Date aggregated every 30 minutes, Shingle Size: 48 1Public Data: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
- 24. Robust Random Cut Forests Quick Summary • Random forests (RF) define ensemble models; • The cut in RCF corresponds to specific choice of partitioning; • Take a set of points, compute bounding box; • Choose an axis proportional to the length (biased), then choose an uniform random cut in range; • Recurse on both sides. • Isolation Forests: Partition at random, dimensions unbiased; • Why RCFs? Can maintain RCF tree distributions efficiently and do so online as data is streaming in. Distance preserving.
- 25. Proceedings 33rd International Conference on Machine Learning, New York, NY, USA, 2016.
- 26. What we are going to see… Random Cut Forests Anomaly Detection Density Estimation Forecasting Semi-Supervised Learning Attribution & Directionality
- 27. Explainable/Transparent/Interpretable ML “If my time-series data with 30 features yields an unusually high anomaly score. How do I explain why this particular point in the time- series is unusual? [..] Ideally I’m looking for some way to visualize “feature importance” for a specific data point.” --- Robin Meehan, Inasight.com
- 28. What is Attribution? It’s the ratio of the “distance” of the anomaly from normal. (It’s a distance in space of repeated patterns in the data.)
- 30. NYC Taxi Ridership Data1 1Public Data: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
- 31. AnomalyScore
- 33. The Moving Example A Fan/Turbine 1000 pts in each blade Gaussian, for simplicity Blades designed unequal Rotate counterclockwise 3 special “query” points 100 trees, 256 points each
- 34. Anomaly Score at P1 What is going on at 90 degrees? Blade overhead = Not an anomaly
- 35. All 3 Blades
- 36. Attribution p1 is far away in x-coord most of the time But what is happening to y? x coordinate’s contribution for p1?
- 37. Directionality Sharp transition when the blade moves from above to below at p1! Total score plummets. Slowly rotating away Total score remains high
- 38. Initial Density and 75 Degrees Rotation Directions towards higher density. Bends towards center!
- 39. Forecasting Exhibit B: Forecasting using RCFs Not just the next value! Ability to “see past” anomalies Auto-detect periodicity … Forecasting implies missing value imputation! Wait, this is just a sine wave … its easy ...
- 40. Maybe not…
- 41. Realistic Data? ECG (one lead) Periodicity unclear … Shingle Size = 185
- 42. Test on hold out data
- 44. A New Explanation for Anomalies? “This point is an anomaly because it is 3x the forecast”. Makes it easier to set triggers/alerts.
- 45. Semi-Supervised Learning on Data Streams One Hour Amazon.com Orders per Minute
- 56. In a long stream…
- 63. Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018.
- 64. What we saw today… Random Cut Forests Anomaly Detection Density Estimation Forecasting Semi-Supervised Learning Attribution & Directionality Detecting Anomalies in Streaming Graphs
- 66. Contributors To This Project Roger Barga, Dhivya Eswaran, Gaurav Ghare, Kapil Chhabra, Sudipto Guha, Shiva Kasiviswanathan, Nina Mishra, Gourav Roy, Joshua Tokle, and Tal Wagner