Machine Learning at Netflix Scale

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Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1tV4sfh. Aish Fenton discusses the machine learning algorithms that power almost every part of the Netflix experience, including some on distributed Neural Networks on AWS GPUs, providing insight into the innovation approach that includes offline experimentation and online AB testing. Filmed at qconnewyork.com. Aish Fenton is Manager of Research and Engineering at Netflix.

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Machine Learning At
Netflix Scale
Aish Fenton
Manager - Research Engineering
@aishfenton

Rank based on your taste
Rank based on your taste

What we were interested in:
▪High quality recommendations
Proxy question:
▪Accuracy in predicted rating
▪Improve by 10% = $1million!
predicted
actual

Top two results still used in production!
SVD RBMs

• > 44M members
• > 40 countries
• > 5B hours in Q3 2013
• Log 100B events/day
• 31.62% of peak US downstream traffic

▪> 40M subscribers
▪Ratings: ~5M/day
▪Searches: >3M/day
▪Plays: > 50M/day
▪Streamed hours:
o 5B hours in Q3 2013
Geo Info
Time
Impressions
Ratings
Device Info
Metadata
Social
Plays
Demographics
Member Behavior

Latent User Vector
Aish House of Cards
Latent Item Vector

House of Cards
m1
m2
m3
House of Cards
3.53
M
u1 u2 u3
Aish Aish
U R

Mean Rating My Bias
Movie Bias
Interaction

3.55 = 2.50 + -1.5 + 1.2 + pq
Mean Rating My Bias
Movie Bias
Interaction
My rating for
House of Cards

House of Cards
3.53
R
U
M
u1 u2 u3
m1
m2
m3
Aish
1.34
2.35
Time
t1 t2 t3 Time
T

▪Matrix/Tensor Factorization
▪Regression models (Logistic, Linear, Elastic nets)
▪Factorization Machines
▪Restricted Boltzmann Machines
▪Markov Chains & other graph models
▪Clustering / Topic Models
▪Neural Networks
▪Association Rules
▪GBDT/RF
▪…

Improvement Over Baseline
Popularity
+ Ratings
+ More Features & Optimized Models
0%
50%
100%
150%
200%
250%
300%

Problem
Data
Experiment
Offline
Test /
Metrics
Produce
Model

Offline
Near-line
Online
Event
Distribution
UI Clients
Online
Algs
Model
Trainer
Pre-compute
AB Test
Metrics
API Layer
Monitoring
Hadoop / Data Warehouse
Experimentation
Platform
S3 / HDFS
Offline
Metrics
Query Tools
Models
Models

Many different types of data…
▪App Logs
▪User Actions
▪Ratings
▪Plays
▪Queue Adds
▪Algo Actions
▪Impressions (Presentation Bias)
▪Context
▪Device Info
▪User Demographics
▪Social
▪Time
▪…

Neural Network Training
1,536 cores
G2 Instances
$0.60 p/h

Offline
Near-line
Publish new model
Online
Event
Distribution
UI Clients
Online
Algs
Model
Trainer
Pre-compute
AB Test
Metrics
API Layer
for Aish
Monitoring
Hadoop / Data Warehouse
Experimentation
Platform
S3 / HDFS
Offline
Metrics
Query Tools
Models
Models
Aish played HoC

Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/machine-learning-
netflix-2014

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2. Watch the video with slide synchronization on InfoQ.com! http://www.infoq.com/presentations /machine-learning-netflix-2014 InfoQ.com: News & Community Site • 750,000 unique visitors/month • Published in 4 languages (English, Chinese, Japanese and Brazilian Portuguese) • Post content from our QCon conferences • News 15-20 / week • Articles 3-4 / week • Presentations (videos) 12-15 / week • Interviews 2-3 / week • Books 1 / month

3. Presented at QCon New York www.qconnewyork.com Purpose of QCon - to empower software development by facilitating the spread of knowledge and innovation Strategy - practitioner-driven conference designed for YOU: influencers of change and innovation in your teams - speakers and topics driving the evolution and innovation - connecting and catalyzing the influencers and innovators Highlights - attended by more than 12,000 delegates since 2007 - held in 9 cities worldwide

4. Machine Learning At Netflix Scale Aish Fenton Manager - Research Engineering @aishfenton

5. Everything is a recommendation

6. 4

7. Top Picks for Aish

8. Movies based on books

9. Because you watched Bob’s Burgers

10.

11. Rank based on your taste Rank based on your taste

12. 75% of plays come from homepage

13. Back Story…

14.

15. What we were interested in: ▪High quality recommendations Proxy question: ▪Accuracy in predicted rating ▪Improve by 10% = $1million! predicted actual

16. Top two results still used in production! SVD RBMs

17. >

18. 2006 2013

19. • > 44M members • > 40 countries • > 5B hours in Q3 2013 • Log 100B events/day • 31.62% of peak US downstream traffic

20. Data and Models

21. ▪> 40M subscribers ▪Ratings: ~5M/day ▪Searches: >3M/day ▪Plays: > 50M/day ▪Streamed hours: o 5B hours in Q3 2013 Geo Info Time Impressions Ratings Device Info Metadata Social Plays Demographics Member Behavior

22. Latent User Vector Aish House of Cards Latent Item Vector

23. House of Cards m1 m2 m3 House of Cards 3.53 M u1 u2 u3 Aish Aish U R

24.

25. Mean Rating My Bias Movie Bias Interaction

26. 3.55 = 2.50 + -1.5 + 1.2 + pq Mean Rating My Bias Movie Bias Interaction My rating for House of Cards

27. House of Cards 3.53 R U M u1 u2 u3 m1 m2 m3 Aish 1.34 2.35 Time t1 t2 t3 Time T

28. ▪Matrix/Tensor Factorization ▪Regression models (Logistic, Linear, Elastic nets) ▪Factorization Machines ▪Restricted Boltzmann Machines ▪Markov Chains & other graph models ▪Clustering / Topic Models ▪Neural Networks ▪Association Rules ▪GBDT/RF ▪…

29. Improvement Over Baseline Popularity + Ratings + More Features & Optimized Models 0% 50% 100% 150% 200% 250% 300%

30. Anatomy of a Machine Learning Platform

31. Problem Data Experiment Offline Test / Metrics Produce Model

32. Offline Near-line Online Event Distribution UI Clients Online Algs Model Trainer Pre-compute AB Test Metrics API Layer Monitoring Hadoop / Data Warehouse Experimentation Platform S3 / HDFS Offline Metrics Query Tools Models Models

33. Offline Near-line Online Event Distribution UI Clients Online Algs Model Trainer Pre-compute AB Test Metrics API Layer Monitoring Hadoop / Data Warehouse Experimentation Platform S3 / HDFS Offline Metrics Query Tools Models Models

34. Many different types of data… ▪App Logs ▪User Actions ▪Ratings ▪Plays ▪Queue Adds ▪Algo Actions ▪Impressions (Presentation Bias) ▪Context ▪Device Info ▪User Demographics ▪Social ▪Time ▪…

35.

36.

37. Offline Near-line Online Event Distribution UI Clients Online Algs Model Trainer Pre-compute AB Test Metrics API Layer Monitoring Hadoop / Data Warehouse Experimentation Platform S3 / HDFS Offline Metrics Query Tools Models Models Embedded Embedded

38. Weights Real-time popularity of movie

39. Example: Neural Network Training

40.

41. θ Input Hidden Layer Output

42. Input Hidden Layers Output

43. Neural Network Training 1,536 cores G2 Instances $0.60 p/h

44. But… things can go astray

45.

46.

47. Offline Near-line Online Event Distribution UI Clients Online Algs Model Trainer Pre-compute AB Test Metrics API Layer Monitoring Hadoop / Data Warehouse Experimentation Platform S3 / HDFS Offline Metrics Query Tools Models Models

48. M U R Pre-compute Online u1 u2 u3

49. Offline Near-line Publish new model Online Event Distribution UI Clients Online Algs Model Trainer Pre-compute AB Test Metrics API Layer for Aish Monitoring Hadoop / Data Warehouse Experimentation Platform S3 / HDFS Offline Metrics Query Tools Models Models Aish played HoC

50. Aish Fenton @aishfenton https://www.linkedin.com/profile/view?id=47917219

51. Watch the video with slide synchronization on InfoQ.com! http://www.infoq.com/presentations/machine-learning- netflix-2014

Machine Learning at Netflix Scale

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