Situation

The client sought to automate and enhance accuracy in their invoice verification process using Machine Learning. Existing manual workflows were inefficient and error-prone.

Task

Design and implement a scalable Python-based ML microservice application to streamline invoice verification, integrate with existing systems, and adhere to modern DevOps practices.

Action

• Designed and built a Python-based microservice using PyTorch for ML, FastAPI for APIs, and pydantic for data validation.

• Integrated PostgreSQL and Amazon S3 for data management and built robust pipelines for preprocessing and model integration.

• Containerized the application with Docker and deployed using Kubernetes, leveraging Helm charts for simplified management.

• Implemented Kafka for real-time data streaming and event-driven architecture.

• Established data governance practices to ensure data quality and scalability.

Result

The solution improved invoice verification accuracy and efficiency, reduced manual effort, and delivered a scalable, automated system for future enhancements.