It Takes a Village to Raise a Machine Learning Model. Lucian

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1 It Takes a Village to Raise a Machine Learning Model Lucian Lita

2 It Takes a Village to Raise a Machine Learning Model Lucian Lita

3 Algorithms

4 Data Big Data n 5yr more data is better than complex algorithms #BigData Big Data n 4yr more clean data is better than more data #BigData Big Data n 3yr more labeled data is better than more data #BigData Big Data n 2yr more smart data is better than purple data #BigData **inflated historical depiction

5 Data

6 Next Frontier: well designed software architectures Personalization, experimentation, anomaly detection, fraud detection

7 Battle Plan Personalization deep dive sw architecture flavor Anomaly detection quick peek Music streaming, advertising, medical informatics brief stories

8

9 x 1 x 1 x 1 x 1 x all x 1 Product as is. No customization. Reasonable coverage. Segmentation. Reasonable coverage. Personalization.

10 Childhood. Approaches.

11 Broad Deep

12 Push-button Push-scientist App App API delivery storage Optimization -- ML algorithms -- data: more, better, smarter -- features, selection

13 Push-button Push-scientist App API delivery storage App API delivery storage Scale & Automation -- model build -- model deploy -- single instrumentation Optimization -- ML algorithms -- data: more, better, smarter -- features, selection

14 Push-scientist Invest in ML; start with a thin system How much effort put into Platform & Automation? (A) best you can do in x weeks (B) one step above prototype (C) enough baling wire & duct tape to support a first use case

15 Push-button Invest in scale & automation; basic ML How much effort put into ML? (A) best generic model setup in y weeks? (B) noticeably better than random? (C) pack enough punch to be visible, but not more

16 Push-button Push-scientist

17 Adolescence. Platform Patterns.

18 (A) Stored App feedback API (capture) personalized content API (retrieve) pre-computed content periodically batch train model periodically run models

19 (B) On-the Fly App feedback API (capture) personalized content API (compute) compute on-the-fly periodically batch train model

20 (C) Aggressive App feedback API (capture) personalized content API (deliver) Challenge accepted: asymptotically real time!

21 (C) Aggressive App feedback API (capture) personalized content API (deliver) Challenge accepted: asymptotically real time!

22 Maturity. Patterns and Assumptions.

23 Model Building Model Deployment Data Store Content Delivery What do you really need? Do you need it now? Analytics Data Capture

24 Model Building. What do you really need? algos space data eval compute operators metrics security scalability HA

25 Model Building. What do you really need? algos space data eval compute operators metrics security scalability HA

26 Model Deployment. What do you really need? API M i M i+1 envt ditto versioning deploy sharing performance security scalability HA

27 Personalization Delivery. What do you really need?

28 Personalization Delivery. What do you really need? API instrument ditto exploit explore sharing performance security scalability HA

29 Data Store. What do you really need? API t content ditto performance HA history scalability triggers consumers governance sharing

30 Data Store. To HA or not to HA. now later (blasphemy) revenue driver in-app critical user benefit infrastructure cost known use cases build & operate

31 Data Store. APIs

32 Data Capture. What do you really need? API content ditto triggers consumers history t sharing performance security scalability HA

33 Analytics. What do you really need? API t content ditto performance history scalability flexibility consumers

34 Analytics. Experimentation & Personalization

35 Data Lake. What do you really need? say big data lake one more time!

36 Evolving Architecture. Before you know it

37 Apps in-app data personalized content personalized content direct content feedback API (compute) API (delivery) API (push) API (capture) run models raw data or features 1 Event Log 3 3 Model Deployment Model Building train models periodically re-run new models periodically 4 RT Analytics API (analytics) **terribly incomplete, mildly inaccurate

38 Not an Exact Blueprint

39 Know this non-trivial no one-size fits all Upfront what do you really need? know thy target architecture As you embark Do it! working system in weeks fast iterations ship & test interfaaaaaaaces!

40 village model **not drawn to effort scale

41 Software architecture is the next frontier! Fail fast still applies! Personalize your personalization platform!

42 better algorithms more, better, smarter data well designed software architectures next frontier

43 A Brief Look at Anomaly Detection

44 Applications System health servers, network Cyber-intrusion detection Enterprise anomaly detection Image processing Textual anomaly detection Sensor networks Fraud detection Medical anomaly detection Industrial damage detection

45 Algorithms Supervised Unsupervised Generic statistical Information theory What algorithms are you going to use?

46 Data Low data volume Invest in data acquisition Invest in high coverage High data volume Invest in defining signal Invest in labeling, tools, and crowdsourcing

47 Architectures Again Data Collectors Clickstream, User Input Real time, DBs Capture Labeling Crowdsourcing Active learning Labeling Processors (M&A) broad: time bounded deep: open ended Compute run models **check assumptions

48 Advertising

49 Music Streaming

50 Medical Informatics

51 better algorithms more, better, smarter data well designed software architectures next frontier

52 Thank you! Lucian Lita [always hiring]

53 Thank you! Lucian Lita [always hiring]

54

55 Extra Content

56 Security. What do you really need?

57

58 App. Who does the App talk to? (a) (b) App personalized content dynamic data App personalized content API (retrieve) -- apply op logic -- retrieve pre-computed content API (compute) -- retrieve static data -- apply op logic -- compute features -- run model -- log actions

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