Talk about practical machine learning lessons learned over the years. Given at the Hardcore Data Science track in Strata San Jose 2016

1. LessonsLearned
from building real-life Machine Learning Systems
Xavier Amatriain (@xamat)
www.quora.com/profile/Xavier-Amatriain
3/29/16

2. A bit about

3. Our Mission
“To share and grow
the world’s knowledge”
• Millions of questions & answers
• Millions of users
• Thousands of topics
• ...

4. Demand
What we care about
Quality
Relevance

5. LessonsLearned

6. MoreDatavs.BetterModels

7. More data or better models?
Really?
Anand Rajaraman: VC, Founder, Stanford Professor

8. More data or better models?
Sometimes, it’s
not about more
data

9. More data or better models?
Norvig:
“Google does not have
better Algorithms only
more Data”
Many features/
low-bias models

10. More data or better models?
Sometimes, it’s
not about more
data

11. Sometimesyoudoneed
A(more)ComplexModel

12. Better models and features that “don’t work”
● E.g. You have a linear model and have
been selecting and optimizing features
for that model
■ More complex model with the same features
-> improvement not likely
■ More expressive features with the same model
-> improvement not likely
● More complex features may require a
more complex model
● A more complex model may not show
improvements with a feature set that is
too simple

13. Modelselectionisalsoabout
Hyperparameteroptimization

14. Hyperparameter optimization
● Automate hyperparameter
optimization by choosing the
right metric.
○ But, is it as simple as choosing the
max?
● Bayesian Optimization
(Gaussian Processes) better
than grid search
○ See spearmint, hyperopt, AutoML,
MOE...

15. Supervisedvs.plus
UnsupervisedLearning

16. Supervised/Unsupervised Learning
● Unsupervised learning as dimensionality reduction
● Unsupervised learning as feature engineering
● The “magic” behind combining
unsupervised/supervised learning
○ E.g.1 clustering + knn
○ E.g.2 Matrix Factorization
■ MF can be interpreted as
● Unsupervised:
○ Dimensionality Reduction a la PCA
○ Clustering (e.g. NMF)
● Supervised
○ Labeled targets ~ regression

17. Supervised/Unsupervised Learning
● One of the “tricks” in Deep Learning is how it
combines unsupervised/supervised learning
○ E.g. Stacked Autoencoders
○ E.g. training of convolutional nets

18. Everythingisanensemble

19. Ensembles
● Netflix Prize was won by an ensemble
○ Initially Bellkor was using GDBTs
○ BigChaos introduced ANN-based ensemble
● Most practical applications of ML run an ensemble
○ Why wouldn’t you?
○ At least as good as the best of your methods
○ Can add completely different approaches (e.
g. CF and content-based)
○ You can use many different models at the
ensemble layer: LR, GDBTs, RFs, ANNs...

20. Ensembles & Feature Engineering
● Ensembles are the way to turn any model into a feature!
● E.g. Don’t know if the way to go is to use Factorization
Machines, Tensor Factorization, or RNNs?
○ Treat each model as a “feature”
○ Feed them into an ensemble

21. The Master Algorithm?
It definitely is the ensemble!

22. Thepains&gains
ofFeatureEngineering

23. Feature Engineering
● Main properties of a well-behaved ML feature
○ Reusable
○ Transformable
○ Interpretable
○ Reliable
● Reusability: You should be able to reuse features in different
models, applications, and teams
● Transformability: Besides directly reusing a feature, it
should be easy to use a transformation of it (e.g. log(f), max(f),
∑ft
over a time window…)

24. Feature Engineering
● Main properties of a well-behaved ML feature
○ Reusable
○ Transformable
○ Interpretable
○ Reliable
● Interpretability: In order to do any of the previous, you
need to be able to understand the meaning of features and
interpret their values.
● Reliability: It should be easy to monitor and detect bugs/issues
in features

25. Feature Engineering Example - Quora Answer Ranking
What is a good Quora answer?
• truthful
• reusable
• provides explanation
• well formatted
• ...

26. Feature Engineering Example - Quora Answer Ranking
How are those dimensions translated
into features?
• Features that relate to the answer
quality itself
• Interaction features
(upvotes/downvotes, clicks,
comments…)
• User features (e.g. expertise in topic)

27. Implicitsignalsbeat
explicitones
(almostalways)

28. Implicit vs. Explicit
● Many have acknowledged
that implicit feedback is more useful
● Is implicit feedback really always
more useful?
● If so, why?

29. ● Implicit data is (usually):
○ More dense, and available for all users
○ Better representative of user behavior vs.
user reflection
○ More related to final objective function
○ Better correlated with AB test results
● E.g. Rating vs watching
Implicit vs. Explicit

30. ● However
○ It is not always the case that
direct implicit feedback correlates
well with long-term retention
○ E.g. clickbait
● Solution:
○ Combine different forms of
implicit + explicit to better represent
long-term goal
Implicit vs. Explicit

31. bethoughtfulaboutyour
TrainingData

32. Defining training/testing data
● Training a simple binary classifier for good/bad
answer
○ Defining positive and negative labels ->
Non-trivial task
○ Is this a positive or a negative?
● funny uninformative answer with many upvotes
● short uninformative answer by a well-known
expert in the field
● very long informative answer that nobody
reads/upvotes
● informative answer with grammar/spelling
mistakes
● ...

33. Other training data issues: Time traveling
● Time traveling: usage of features that originated after the
event you are trying to predict
○ E.g. Your upvoting an answer is a pretty good prediction
of you reading that answer, especially because most
upvotes happen AFTER you read the answer
○ Tricky when you have many related features
○ Whenever I see an offline experiment with huge wins, I
ask: “Is there time traveling?”

34. YourModelwilllearn
whatyouteachittolearn

35. Training a model
● Model will learn according to:
○ Training data (e.g. implicit and explicit)
○ Target function (e.g. probability of user reading an answer)
○ Metric (e.g. precision vs. recall)
● Example 1 (made up):
○ Optimize probability of a user going to the cinema to
watch a movie and rate it “highly” by using purchase history
and previous ratings. Use NDCG of the ranking as final
metric using only movies rated 4 or higher as positives.

36. Example 2 - Quora’s feed
● Training data = implicit + explicit
● Target function: Value of showing a story to a
user ~ weighted sum of actions: v = ∑a
va
1{ya
= 1}
○ predict probabilities for each action, then compute expected
value: v_pred = E[ V | x ] = ∑a
va
p(a | x)
● Metric: any ranking metric

37. Offline testing
● Measure model performance,
using (IR) metrics
● Offline performance = indication
to make decisions on follow-up
A/B tests
● A critical (and mostly unsolved)
issue is how offline metrics
correlate with A/B test results.

38. Learntodealwith
PresentationBias

39. 2D Navigational modeling
More likely
to see
Less likely

40. The curse of presentation bias
● User can only click on what you decide to show
● But, what you decide to show is the result of what your model
predicted is good
● Simply treating things you show as negatives is not likely to work
● Better options
● Correcting for the probability a user will click on a position ->
Attention models
● Explore/exploit approaches such as MAB

41. Youdon’tneedtodistribute
yourMLalgorithm

42. Distributing ML
● Most of what people do in practice can fit into a multi-
core machine
○ Smart data sampling
○ Offline schemes
○ Efficient parallel code
● Dangers of “easy” distributed approaches such
as Hadoop/Spark
● Do you care about costs? How about latencies?

43. Distributing ML
● Example of optimizing computations to fit them into
one machine
○ Spark implementation: 6 hours, 15 machines
○ Developer time: 4 days
○ C++ implementation: 10 minutes, 1 machine
● Most practical applications of Big Data can fit into
a (multicore) implementation

44. Theuntoldstoryof
DataScienceandvs.MLengineering

45. Data Scientists and ML Engineers
● We all know the definition of a Data Scientist
● Where do Data Scientists fit in an organization?
○ Many companies struggling with this
● Valuable to have strong DS who can bring value
from the data
● Strong DS with solid engineering skills are
unicorns and finding them is not scalable
○ DS need engineers to bring things to production
○ Engineers have enough on their plate to be willing to
“productionize” cool DS projects

46. The data-driven ML innovation funnel
Data Research
ML Exploration -
Product Design
AB Testing

47. Data Scientists and ML Engineers
● Solution:
○ (1) Define different parts of the innovation funnel
■ Part 1. Data research & hypothesis
building -> Data Science
■ Part 2. ML solution building &
implementation -> ML Engineering
■ Part 3. Online experimentation, AB
Testing analysis-> Data Science
○ (2) Broaden the definition of ML Engineers
to include from coding experts with high-level
ML knowledge to ML experts with good
software skills
Data Research
ML Solution
AB Testing
Data
Science
Data
Science
ML
Engineering

48. Conclusions

49. ● In data, size is not all that matters
● Understand dependencies between data, models
& systems
● Choose the right metric & optimize what matters
● Be thoughtful about
○ your ML infrastructure/tools
○ about organizing your teams

50. Questions?