ML Model Deploy Serve Workflow
Deploy a trained ML model as a serving endpoint with A/B testing, monitoring, and rollback capability.
Objective
Deploy a machine learning model to production with monitoring and rollback.
Environment
- A trained ML model artifact
- A model serving framework
- A monitoring system for model performance
Steps
1. Package the model
Serialize the trained model and its dependencies into a deployable artifact.
Expected state: A model artifact is packaged and ready for deployment.
2. Deploy the endpoint
Deploy the model as a serving endpoint with the serving framework.
Expected state: The model is accessible via a prediction API.
3. Set up A/B testing
Route a percentage of traffic to the new model for comparison against the current model.
Expected state: A/B testing routes a portion of traffic to the new model.
4. Monitor performance
Track prediction latency, error rate, and model accuracy metrics.
Expected state: Model performance metrics are being collected.
5. Full rollout or rollback
Compare A/B results and fully deploy the new model or roll back if metrics regress.
Expected state: The new model is fully deployed or safely rolled back.
Decision points
Canary or A/B testing?
- Canary for simple rollout
- A/B for model comparison
Use A/B testing when comparing model quality; canary for simple, risk-free rollouts.
Expected outcome
A model is deployed, A/B tested, and either fully rolled out or rolled back based on metrics.
Verification
Send test predictions and confirm the model responds correctly; verify A/B routing works as configured.
Cleanup
Roll back to the previous model if prediction quality regresses; keep the model artifact for analysis.
Models need the same safety as code
A model in production needs monitoring, rollback, and gradual rollout just like application code.
Implementation Notes
This workflow provides a structured approach to ensure consistent results across team members and environments.
Common Pitfalls
Skipping verification steps or rushing through the process can lead to incomplete results. Always follow each step in order and verify before proceeding.
Quick Reference
# Execute the workflow steps as documented above
echo "Workflow completed successfully"