MLOps Platform for Model Deployment
ML Platform Engineer
MLOps Infrastructure CI/CD Kubernetes Python
Stack: Python, Kubernetes, Docker, MLflow, Argo, Prometheus
Context
Pain points in existing deployment workflow
Time from trained model to production before
Lack of standardization across teams
Constraints
- Framework Support: PyTorch, scikit-learn, XGBoost, custom models
- Integration: Existing CI/CD pipelines (GitHub Actions)
- Self-Service: Data scientists deploy without platform team
- Compliance: Audit trail for model versions and predictions
Architecture
Model registry design
Containerization strategy
Deployment targets (Kubernetes, serverless options)
Traffic management (canary, shadow, blue-green)
Monitoring integration
Implementation Highlights
Model Packaging
Dependency isolation and reproducibility
Automated Testing
Test pipeline for model quality gates
Rollback
Fast recovery when deployments fail
Autoscaling
Resource allocation based on load
Evaluation
| Metric | Before | After |
|---|---|---|
| Deployment Time | X days | Y hours |
| Rollback Time | TBD | TBD |
| Failed Deployments | TBD | TBD |
| Developer Satisfaction | TBD | TBD |
Developer satisfaction survey results
Incident reduction post-deployment
Outcomes
- Deployment time reduction
- Number of models deployed via platform
- Incident rate change
- Team adoption rate
Learnings
Where Data Scientists Get Stuck
Common friction points
Good Defaults
The value of sensible starting points
Enforce vs. Recommend
When to require vs. suggest best practices