Client: A mid-sized hospital chain with 30+ branches across Southeast Asia
Industry: Healthcare & Life Sciences
Challenge:
The hospital’s data science team was building predictive models for patient readmission risk, but deployments took 8–10 weeks, causing delayed clinical adoption. Models were inconsistently monitored, leading to model drift and reduced accuracy over time.

Solution:
We implemented a cloud-native MLOps pipeline integrating:

• Automated data ingestion from EHR systems
• CI/CD for ML models using GitOps principles
• Model versioning with MLflow
• Continuous performance monitoring and retraining triggers

Results:

• Model deployment time reduced from 8 weeks to 5 days
• Prediction accuracy maintained above 92% for 12 months with automated retraining
• Clinicians received updated risk predictions in near real-time, improving patient care

Key MLOps Features Used:
✅ Automated model deployment
✅ Continuous monitoring for data & concept drift
✅ Audit-ready model governance for compliance