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Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief Data Scientist, BeeswaxIO
Chief Data Scientist @ Beeswax
@sizrailev / bit.ly/MLatScale / sergei@beeswax.com / www.beeswax.com
Sergei Izrailev
Design Patterns
for Machine Learning
in Production
Motivation
• A widespread need to leverage in-house data
• Data science expertise is available
• And yet, it seems to be too hard to extract value from ML
• Beeswax
• Beeswax is an ad tech startup; 40 employees in NYC and London
• Founded by three ex-Googlers
• Real-time bidding (RTB) platform for buying online ads (1M+ QPS)
• Platform tailored for customers to leverage in-house data science
• Myself
• Production AI systems in Pharma, Finance and Ad Tech
• Interested in both technology and organizations
• bit.ly/MLatScale
About
Overall process Discovery
Research
Prototype
Production
Value Cost
Problem
Statement
Constraints
Start with defining the problem
Problem statement
• Is this the right problem to solve?
• Suppose, we’ve solved the stated problem - what’s the value?
• Is ML the right tool to solve the problem?
• What are the constraints?
Define constraints
• Existing production environment architecture
• Technology stack
• Available people and their skills
• Requirements for scale
Dimensions of ML system scalability
• Volume: how much data do we need to process?
• Velocity: how quickly does the data change?
• Variety: what are the types of data, models, and applications?
• Veracity: how accurate are our models?
• Value: how does it matter to the ML consumer?
• Viability: do the benefits outweigh the costs?
Technical Design of ML Systems
Machine learning systems
ApplyBuild
Model
Data Sources
Labeled data Unlabeled data
Consumer
ML system design
Build Apply
Model
Data Sources
Labeled data Unlabeled data
Consumer
?
? ??
?
?
?
Model deployment
Data Sources
Labeled data Unlabeled data
? ?
Consumer
? ?
Build
Model
?
?
Apply
?
Model deployment
Build
Trans-
form
Train
Code + Data
Apply
Predict
Trans-
form
• Data transformations must be the same in training and scoring
• Some transformations are “models” (PCA, top N, TF-IDF)
• Hence, most ML pipelines are DAGs
• These DAGs must be reproduced in production scoring
Interface between building and scoring
• In-memory - model is never persisted, train then score
• single application, also streaming
• Data only - linear coefficients, PMML, etc
• code is independent
• Serialized objects - Pickle, R, Spark, custom
• reuse code
• Code + Data - e.g., H2O’s POJO
• code is generated
Scoring systems
Build
Model
Data Sources
Labeled data Unlabeled data
?
?
?
?
Apply Consumer
?? ?
Batch processing
ConsumerApply
Predictions Predictions
New data Trans-
form
Predict
Batch features; consumer predicts
New data
ConsumerApply
Model
Features
Predict
Features
Trans-
form
Single-row predictions
Consumer
Model
Predict
Trans-
form
New data
A service for a single row consumer
ConsumerApply
New data
Prediction
New data
Trans-
form
Predict
A service for a single row consumer
ConsumerApply
New data
Prediction
New data
Trans-
form
Predict
Cache
Cached predictions
ConsumerApply
Cache
Predictions
PredictionKey
Default
New data
Trans-
form
Predict
New data
Predictions
Near-real-time
ConsumerApply
Cache
Predictions
PredictionKey
Default
New data
Trans-
form
Predict
Add data
Predictions
New data
Cached features
ConsumerApply
Cache
Features
FeaturesKey
Model
Features
Predict
New data Trans-
form
New data
Evolution of ML systems
Research
Build
Sample
Transform
& generate
features
Collect
labels
Train &
Test
Extract &
Clean
Prototype
Build
Sample
Transform
generate
features
Collect
labels
Train &
Test
Extract &
Clean
Apply
Transform
Generate
features
Predict
Evaluate
Extract &
Clean
Serialized
Model
Production
Build Apply
Serialized
Model
Visualization CI/CD
Monitoring
& Alerts
Model
Evaluation
Change
Mgmt
Error
Handling
Data
Validation
Versioning
Security &
Access
Reporting
Testing &
QA
Logging
Production
Fault-tolerant systems
• We should expect problems
• models don’t converge; input data changes; bugs
• Produce acceptable results - even when something fails
• Handle predictable error conditions automatically
• Minimal human intervention
• Easy diagnostics and recovery in case of fatal errors
Conway's law:
"Any piece of software reflects the
organizational structure that produced it."
People Questions
• Who is developing training?
• Who is developing scoring?
• Who is responsible for training in production?
• Who is responsible for scoring in production?
• Who deploys new models and model updates?
• Who is responsible for quality control?
Product management
Data science
Data engineering
Application engineering (RT
server-side applications, client-
side applications)
UX
People’s Functions
• Front-end development
• Data collection (e.g.,
logging)
• Code deployment
• Testing and QA
• Infrastructure provisioning
• Support
Data scientists
Data scientists as consumers
Apply ConsumerBuild
Serialized
Model
EngineersData scientists
Over the wall
Build Apply
Serialized
Model
Build Apply
Serialized
Model
Consumer
EngineersData scientists
Deliver predictions only (aka “black box”)
Build Apply
Serialized
Model
Consumer
Predictions
EngineersData scientists
Deliver data (serialized model)
Apply ConsumerBuild
Serialized
Model
Scoring
Code
EngineersData scientists
Deliver code and data
Apply ConsumerBuild
Serialized
Model
Scoring
Code
Team: Data Scientists + Engineers
Cross-functional team
Apply ConsumerBuild
Serialized
Model
Scoring
Code
EngineersData scientists
ML platform
Build Apply
Serialized
Model
Consumer
Apply
Build
ML platform
Serialized
Model
Visualization CI/CD
Monitoring
& Alerts
Model
Evaluation
Change
Mgmt
Error
Handling
Data
Validation
Versioning
Security &
Access
Reporting
Testing &
QA
Logging
ApplyBuild
Conclusion
• Find the right problem
• Define constraints
• Design components and interfaces
• Take into account organizational constraints
• Production can’t be an afterthought
• The process is a lot of work, but it’s not rocket science
Questions?
Yes, we are hiring…
sergei@beeswax.com

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