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Continuous Delivery for
Machine Learning Systems
Deploying ML Systems to
Production safely and quickly
in a sustainable way
Adarsh Shah
Engineering Leader, Coach, Hands-on Architect
Independent Consultant
@shahadarsh
https://shahadarsh.com
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https://shahadarsh.com @shahadarsh
Hidden Technical Debt in ML Systems
From the paper Hidden Technical Debt in Machine Learning Systems
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https://shahadarsh.com @shahadarsh
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Results
Traditional Software Development Machine Learning
Program Data
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Desired
Results
Model
Training
Data
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Program
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Live Data
Training Prediction
Results
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Data
Acquisition
Data
Preparation
Model
Development
Training Prediction
Accuracy
Evaluation
Data Management Experimentation Production Deployment
Validation
Monitoring
/ Alerting
Accuracy not reached
Retrain
Data Drift Fix
Accuracy
reached
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shahadarsh.com @shahadarsh
Challenges Unique to ML
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#1: Data Management
Data Location
Large Datasets
Security
Compliance
Data Quality
Tracking Dataset
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#2: Experimentation
Code Quality
Research &
Experimentation
Tracking
experiments
Training Time
& Troubleshooting
Infrastructure
Requirements
Model Accuracy
Evaluation
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#3: Production Deployment
Offline/Online
Prediction
Monitoring & Alerting
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What is Continuous Delivery?
Continuous Delivery is the ability to get changes of all
types—including new features, configuration changes,
bug fixes and experiments—into production, or into the
hands of users, safely and quickly in a sustainable
way.
- Jez Humble & Dave Farley
(Continuous Delivery Book Authors)
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Continuous Delivery
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Continuous Integration
Continuous Integration is a software development
practice where members of a team integrate their work
frequently, usually each person integrates at least daily -
leading to multiple integrations per day.
- Martin Fowler
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Principles of Continuous Delivery
๏ Build quality in
๏ Work in small batches
๏ Computers perform repetitive tasks, people solve
problems
๏ Relentlessly pursue continuous improvement (Kaizen)
๏ Everyone is responsible
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Data pipeline
Data
Source A
Data
Source B
Data
Source C
Data
Acquisition
A
Data
Validation
A
Data
Preparation
A
Training
Dataset
Versioned
Training
Process
Testing
Data
Acquisition
B
Data
Validation
B
Data
Preparation
B
Data
Acquisition
C
Data
Validation
C
Data
Preparation
C
Bias & Fairness
——
Security
& Compliance
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Static
Analysis
Unit Tests
Training Code
Linting etc.
Artifact
Repository
Build
Artifact
Continuous Integration (Training Code)
Dev
Environment
Validation
Tests
Merge to
Main Branch
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Data Pipeline Continuous Integration
(Training Code) Configuration
Training
Dataset
Training
Environment
Accuracy
Evaluation
Monitoring/
Alerting
Testing (Bias
& Fairness)
Model
Trigger
Log
Aggregation
Automated
Provisioning/De-provisioning
Data
Scientist
Training
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Static
Analysis
Unit Tests
Application
Code
Linting, Security Scan etc.
Artifact
Repository
Build
Artifact
Ephemeral
Environment
Integration
Tests
Tag as
Tested
Model
Continuous Integration (Application Code)
Training
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Data Management Experimentation Production Deployment
Data Pipeline Continuous Integration
(Training Code)
Data
Scientist
Configuration
Training Model
Continuous Integration
(Application Code)
Deployment
Production
Environment
Smoke
Tests
Monitoring
/Alerting
Application
Developer
Bringing it all together
Training
Dataset
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Machine Learning Roles
ML Researcher
ML Engineer
Data Engineer
MLOps Engineer
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Team Structure Considerations
Cross Functional Team Separate Data Science Team ML Platform Engineering Team
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Platforms
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References
• continuousdelivery.com
• Dr. Deming’s 14 Points for Management
• Challenges Deploying Machine Learning Models to
Production
• State of DevOps Report
• martinfowler.com
• Large image datasets: A pyrrhic win for computer vision?
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Book Recommendations
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https://shahadarsh.com @shahadarsh
Adarsh Shah
Engineering Leader, Coach, Hands-on Architect
Independent Consultant
@shahadarsh
https://shahadarsh.com