Overview

We implemented a comprehensive ML Ops (Machine Learning Operations) framework for a healthcare insurance provider to automate their claims review process.

Technology Used

• OpenAI

   

• Databricks

   

• Terraform

   

• GitHub Actions

   

Details

Our solution focused on enhancing the entire ML lifecycle—from data ingestion and model training to deployment and monitoring—ensuring efficient handling of large-scale healthcare data. By integrating automated pipelines, we improved model accuracy, reduced manual intervention, and accelerated the delivery of predictive analytics. 

This allowed the company to leverage real-time insights for tasks such as risk assessment, claims processing, and fraud detection, ultimately improving decision-making and operational efficiency. The scalable ML Ops infrastructure also ensures compliance with healthcare regulations, supports continuous model updates, and enhances the company’s ability to detect fraud and prevent fraud.