Continuous Delivery for Machine Learning 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 nycdevops 

https://shahadarsh.com @shahadarsh Adarsh Shah Engineering Leader, Coach, Hands-on Architect Independent Consultant @shahadarsh 
 https://shahadarsh.com 

https://shahadarsh.com @shahadarsh Hidden Technical Debt in ML Systems From the paper Hidden Technical Debt in Machine Learning Systems 

https://shahadarsh.com @shahadarsh 1 0 1 0 1 0 1 0 1 Results Traditional Software Development Machine Learning Program Data { } 1 0 1 0 1 0 1 0 1 Desired Results Model Training Data { } Program { } 1 0 1 0 1 0 1 0 1 Live Data Training Prediction Results 

https://shahadarsh.com @shahadarsh 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 

shahadarsh.com @shahadarsh Challenges Unique to ML 

https://shahadarsh.com @shahadarsh #1: Data Management Data Location Large Datasets Security Compliance Data Quality Tracking Dataset 

https://shahadarsh.com @shahadarsh #2: Experimentation Code Quality Research & 
 Experimentation Tracking experiments Training Time 
 & Troubleshooting Infrastructure 
 Requirements Model Accuracy Evaluation 

https://shahadarsh.com @shahadarsh #3: Production Deployment Offline/Online 
 Prediction Monitoring & Alerting 

https://shahadarsh.com @shahadarsh #4: Dependency Hell Dependency Hell ARM architecture 

https://shahadarsh.com @shahadarsh 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) 

https://shahadarsh.com @shahadarsh Continuous Delivery 

https://shahadarsh.com @shahadarsh 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 

https://shahadarsh.com @shahadarsh Continuous Delivery Push Code Unit Tests Auto Auto Integration Tests Auto Acceptance Tests Auto Deploy to Production Continuous Deployment Push Code Unit Tests Auto Auto Integration Tests Auto Acceptance Tests Auto Deploy to Production Auto Manual 

https://shahadarsh.com @shahadarsh 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 

https://shahadarsh.com @shahadarsh Toyota Production System 

https://shahadarsh.com @shahadarsh 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 

https://shahadarsh.com @shahadarsh Static Analysis Unit Tests Training Code Linting etc. Artifact Repository Build Artifact Continuous Integration (Training Code) Dev Environment Validation Tests Merge to 
 Main Branch 

https://shahadarsh.com @shahadarsh 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 

https://shahadarsh.com @shahadarsh 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 

https://shahadarsh.com @shahadarsh 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 

https://shahadarsh.com @shahadarsh Machine Learning Roles ML Researcher ML Engineer Data Engineer MLOps Engineer 

https://shahadarsh.com @shahadarsh Team Structure Considerations Cross Functional Team Separate Data Science Team ML Platform Engineering Team 

shahadarsh.com @shahadarsh Platforms available 

https://shahadarsh.com @shahadarsh Platforms 

https://shahadarsh.com @shahadarsh Kubeflow 

https://shahadarsh.com @shahadarsh 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? 

https://shahadarsh.com @shahadarsh Book Recommendations 

Questions Adarsh Shah Engineering Leader, Coach, Hands-on Architect Independent Consultant @shahadarsh 
 https://shahadarsh.com nycdevops