MLOps Pipeline: Model Deployment from 2 Weeks to 2 Hours
A product company's data science team deployed models by SSH-ing into servers and running scripts manually. We built an end-to-end MLOps pipeline that cut deployment time from 2 weeks to under 2 hours.
The Challenge
What was getting in the way
- 01
Model deployment was a manual process. A data scientist would train locally, hand off a pickle file, and an engineer would deploy it via SSH. The whole cycle took about 2 weeks
- 02
No model versioning or rollback capability. When a model performed badly in production, the team had to retrain from scratch
- 03
Production monitoring was nonexistent. The team found out about model drift when customers complained, not from alerts
The Solution
How we solved it
We set up MLflow for experiment tracking and model registry. Every training run gets logged with parameters, metrics, and artifacts. When a model is approved, a GitHub Actions pipeline packages it into a Docker container, runs integration tests, and deploys to a SageMaker endpoint with canary routing. We added Evidently for data drift and model performance monitoring, with alerts going to Slack when metrics drop below thresholds. Rollback is one click. The team can now train, validate, and ship a model update in under 2 hours without touching a server.
Technologies
What We Built
A look inside the project
The Process
Step-by-step delivery
Experiment Tracking
Log all training runs with MLflow: params, metrics, artifacts
Model Registry
Version models, tag for staging/production, approval workflow
CI/CD Pipeline
Automated build, test, and deploy via GitHub Actions + Docker
Canary Deployment
Route 10% traffic to new model, compare metrics before full rollout
Drift Monitoring
Track data drift and model performance, alert on Slack
The Results
The numbers
Model Deployment Time (was 2 weeks)
Production Incidents Since Launch
More Model Iterations Per Quarter