Talk given on 2017-08-11 from Reliable ML in the Wild Workshop at ICML 2017.

1. Making Netflix Machine
Learning Algorithms
Reliable
Tony Jebara & Justin Basilico
ICML Reliable ML in the Wild Workshop
2017-08-11

2. 2
2006
6M members
US only

3. 3
2017
> 100M members
> 190 countries

4. Goal:
Help members find content to watch and enjoy to
maximize member satisfaction and retention

5. Algorithm Areas
▪ Personalized Ranking
▪ Top-N Ranking
▪ Trending Now
▪ Continue Watching
▪ Video-Video Similarity
▪ Personalized Page Generation
▪ Search
▪ Personalized Image Selection
▪ ...

6. Models & Algorithms
▪ Regression (linear, logistic, ...)
▪ Matrix Factorization
▪ Factorization Machines
▪ Clustering & Topic Models
▪ Bayesian Nonparametrics
▪ Tree Ensembles (RF, GBDTs, …)
▪ Neural Networks (Deep, RBMs, …)
▪ Gaussian Processes
▪ Bandits
▪ …

7. A/B tests validate an overall approach works in
expectation
But they run in online production, so every A/B
tested model needs to be reliable
Innovation Cycle
Idea
Offline
Experiment
Full Online
Deployment
Online A/B
Test

8. 1) Collect massive data sets
2) Try billions of hypotheses to find* one(s) with support
*Find with computational and statistical efficiency
Batch Learning

9. USERS
TIME
Collect Learn A/B Test Roll-out
Data Model
A
B
Batch Learning

10. USERS
TIME
A
BREGRET
Batch Learning
Collect Learn A/B Test Roll-out
Data Model

11. Explore and exploit → Less regret
Helps cold start models in the wild
Maintain some exploration for nonstationarity
Adapt reliably to changing and new data
Epsilon-greedy, UCB, Thompson Sampling, etc.
USERS
TIME
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Bandit Learning

12. USERS
TIME
Bandit Learning
1) Uniform population of hypotheses
2) Choose a random hypothesis h
3) Act according to h and observe outcome
4) Re-weight hypotheses
5) Go to 2
TS

13. Bandits for selecting images

14. Bandits for selecting images

15. Different preferences for genre/theme portrayed
Contextual bandits to personalize images

16. Different preferences for cast members
Contextual bandits to personalize images

17. Putting Machine
Learning In Production

18. 18
Training Pipeline
Application
“Typical” ML Pipeline: A tale of two worlds
Historical
Data
Generate
Features
Train Models
Validate &
Select
Models
Application
Logic
Live
Data
Load
Model
Offline
Online
Collect
Labels
Publish
Model
Evaluate
Model
Experimentation

19. Offline: Model trains reliably
Online: Model performs reliably
… plus all involved systems and data
What needs to be reliable?

20. Detection
Response
Prevention
Reliability Approach

21. Reliability in Training

22. To be reliable, first learning must be repeatable
Automate retraining of models
Akin to continuous deployment in software engineering
How often depends on application; typically daily
Detect problems and fail fast to avoid using a bad model
Retraining

23. Example Training Pipeline
● Periodic retraining to refresh models
● Workflow system to manage pipeline
● Each step is a Spark or Docker job
● Each step has checks in place
○ Response: Stop workflow and send alerts

24. What to check?
● For both absolute and relative to previous runs...
● Offline metrics on hold-out set
● Data size for train and test
● Feature distributions
● Feature importance
● Large (unexpected) changes in output between models
● Model coverage (e.g. number of items it can predict)
● Model integrity
● Error counters
● ...

25. Example 1: number of samples
Numberoftrainingsamples
Time

26. Example 1: number of samples
Numberoftrainingsamples
Time
Alarm fires and
model is not
published due to
anomaly

27. Example 2: offline metrics
Liftw.r.tbaseline
Time

28. Example 2: offline metrics
Liftw.r.tbaseline
Time
Alarm fires and model is
not published due to
anomaly
Absolute threshold: if lift
< X then alarm also fires

29. Testing
- Unit testing and code coverage
- Integration testing
Improve quality and reliability of upstream data feeds
Reuse same data and code offline and online to avoid
training/serving skew
Running from multiple random seeds
Preventing offline training failures

30. Reliability Online

31. Check for invariants in inputs and outputs
Can catch many unexpected changes, bad assumptions
and engineering issues
Examples:
- Feature values are out-of-range
- Output is NaN or Infinity
- Probabilities are < 0 or > 1 or don’t sum to 1
Basic sanity checks

32. Online staleness checks can cover a wide range of failures in the training
and publishing
Example checks:
- How long ago was the most recent model published?
- How old is the data it was trained on?
- How old are the inputs it is consuming?
Model and input staleness
Stale
Model
Ageofmodel

33. Track the quality of the model
- Compare prediction to actual
behavior
- Online equivalents of offline metrics
For online learning or bandits reserve a
fraction of data for a simple policy (e.g.
epsilon-greedy) as a sanity check
Online metrics

34. Your model isn’t working right or your input data
is bad: Now what?
Common approaches in personalization space:
- Use previous model?
- Use previous output?
- Use simplified model or heuristic?
- If calling system is resilient: turn off that
subsystem?
Response: Graceful Degradation

35. Want to choose personalized images per profile
- Image lookup has O(10M) requests per second (e.g.
“House of Cards” -> Image URL)
Approach:
- Precompute show to image mapping per user
near-line system
- Store mapping in fast distributed cache
- At request time
- Lookup user-specific mapping in cache
- Fallback to unpersonalized results
- Store mapping for request
- Secondary fallback to default image for a
missing show
Example: Image Precompute Personalized Selection
(Precompute)
Unpersonalized
Default Image

36. Netflix runs 100% in AWS cloud
Needs to be reliable on unreliable infrastructure
Want a service to operate when an AWS instance
fails?
Randomly terminate instances (Chaos Monkey)
Want Netflix to operate when an entire AWS region
is having a problem?
Disable entire AWS regions (Chaos Kong)
Prevention: Failure Injection

37. What failure scenarios do you want your model to be
robust to?
Models are very sensitive to their input data
- Can be noisy, corrupt, delayed, incomplete, missing,
unknown
Train model to be resilient by injecting these conditions
into the training and testing data
Failure Injection for ML

38. Want to add a feature for type of row on homepage
Genre, Because you watched, Top picks, ...
Problem: Model may see new types online before in training data
Solution: Add unknown category and perturb a small fraction of
training data to that type
Rule-of-thumb: Apply for all categorical features unless new
category isn’t possible (Days of week -> OK, countries -> Not)
Example: Categorical features

39. Conclusions.

40. ● Consider online learning and bandits
● Build off best practices from software engineering
● Automate as much as possible
● Adjust your data to cover the conditions you want
your model to be resilient to
● Detect problems and degrade gracefully
Take aways

41. Thank you.
Tony Jebara & Justin Basilico
Yes, we are hiring!