Client
A leading healthcare provider utilizing AI to predict patient readmissions and recommend personalized treatments.
Challenge
Their machine learning models were difficult to deploy and maintain, with inconsistencies in model performance and delayed updates to patient prediction tools.
Solution
- MLOps Integration: Streamlined the model deployment process using Kubernetes, enabling seamless scalability for handling large patient datasets.
- Automated Data Pipelines: Built robust pipelines to clean and preprocess patient data in real-time.
- Model Monitoring Integrated performance monitoring to detect drifts and retrain models as needed.
Results
- Reduced model deployment time from weeks to hours.
- Improved model accuracy by 15% through regular retraining.
- Enabled real-time patient insights, resulting in 25% fewer readmissions.
