Client: Healthcare AI Startup
Tools: MLflow, Kubeflow, Jenkins, Docker, AWS Step Functions
Challenge:
Model training, validation, and deployment were manual, error-prone, and time-consuming.

Solution:

• Created an MLOps pipeline using Kubeflow Pipelines and MLflow tracking
• Automated Docker builds and validation via Jenkins
• Integrated with AWS Step Functions to orchestrate model promotion logic
• Enabled auto-rollback of poor-performing models with trigger thresholds

Outcome:
Model deployment frequency improved by 5x. Reduced time-to-production from weeks to 2 days.