10 more lessons learned from building machine learning systems
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10 More Lessons Learned from building real-life Machine Learning Systems
Xavier Amatriain (@xamat) 10/13/2015
Our Mission
“To share and grow the world’s knowledge”
● Millions of questions & answers
● Millions of users
● Thousands of topics
● ...
ML Applications @ Quora
● Answer ranking
● Feed ranking
● Topic recommendations
● User recommendations
● Email digest
● Ask2Answer
● Duplicate Questions
● Related Questions
● Spam/moderation
● Trending now
● ...
Models● Logistic Regression
● Elastic Nets
● Gradient Boosted Decision
Trees
● Random Forests
● (Deep) Neural Networks
● LambdaMART
● Matrix Factorization
● LDA
● ...
Implicit vs. Explicit
● Many have acknowledged
that implicit feedback is more useful
● Is implicit feedback really always
more useful?
● If so, why?
● 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
● 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
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.
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
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
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
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...
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
Outputs will be inputs
● Ensembles turn any model into a feature
○ That’s great!
○ That can be a mess!
● Make sure the output of your model is ready to
accept data dependencies
○ E.g. can you easily change the distribution of the
value without affecting all other models
depending on it?
● Avoid feedback loops
● Can you treat your ML infrastructure as you would
your software one?
ML vs Software
● Can you treat your ML infrastructure as you would
your software one?
○ Yes and No
● You should apply best Software Engineering
practices (e.g. encapsulation, abstraction, cohesion,
low coupling…)
● However, Design Patterns for Machine Learning
software are not well known/documented
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…)
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
Feature Engineering Example - Quora Answer Ranking
What is a good Quora answer?
• truthful
• reusable
• provides explanation
• well formatted
• ...
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)
Machine Learning Infrastructure
● Whenever you develop any ML infrastructure, you need to
target two different modes:
○ Mode 1: ML experimentation
■ Flexibility
■ Easy-to-use
■ Reusability
○ Mode 2: ML production
■ All of the above + performance & scalability
● Ideally you want the two modes to be as similar as possible
● How to combine them?
Machine Learning Infrastructure: Experimentation & Production
● Option 1:
○ Favor experimentation and only invest in productionizing
once something shows results
○ E.g. Have ML researchers use R and then ask Engineers
to implement things in production when they work
● Option 2:
○ Favor production and have “researchers” struggle to figure
out how to run experiments
○ E.g. Implement highly optimized C++ code and have ML
researchers experiment only through data available in logs/DB
Machine Learning Infrastructure: Experimentation & Production
● Option 1:
○ Favor experimentation and only invest in productionazing once
something shows results
○ E.g. Have ML researchers use R and then ask Engineers to
implement things in production when they work
● Option 2:
○ Favor production and have “researchers” struggle to figure out
how to run experiments
○ E.g. Implement highly optimized C++ code and have ML
researchers experiment only through data available in logs/DB
● Good intermediate options:
○ Have ML “researchers” experiment on iPython Notebooks using
Python tools (scikit-learn, Theano…). Use same tools in
production whenever possible, implement optimized versions
only when needed.
○ Implement abstraction layers on top of optimized
implementations so they can be accessed from regular/friendly
experimentation tools
Machine Learning Infrastructure: Experimentation & Production
Model debuggability
● Value of a model = value it brings to the product
● Product owners/stakeholders have expectations on
the product
● It is important to answer questions to why did
something fail
● Bridge gap between product design and ML algos
● Model debuggability is so important it can
determine:
○ Particular model to use
○ Features to rely on
○ Implementation of tools
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?
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
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
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
ScienceD
ata Science
ML
Engineering
● Make sure you teach your model what you
want it to learn
● Ensembles and the combination of
supervised/unsupervised techniques are key
in many ML applications
● Important to focus on feature engineering
● Be thoughtful about
○ your ML infrastructure/tools
○ about organizing your teams
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