In this project, I developed an end-to-end MLOps pipeline to automate the entire lifecycle of machine learning models, ensuring a seamless and reliable workflow. Key features of the pipeline include:

Automated Model Promotion: Streamlined the promotion of models between environments, from development to production, ensuring compliance and efficiency.

Unit Testing and Validation: Incorporated automated unit tests and validation steps to guarantee model reliability and adherence to performance benchmarks.

Champion-Challenger Framework: Designed a robust system for model comparison, allowing new challengers to be tested against current champions before promotion.

Retraining and Batch Inference Jobs: Implemented scheduled jobs for automated model retraining with updated data and batch inference tasks for large-scale predictions.

CI/CD Integration: Integrated the entire pipeline with the client’s CI/CD tool, enabling version control, deployment automation, and monitoring within their existing ecosystem.

This project significantly enhanced the client’s MLOps capabilities, reducing manual interventions, improving model deployment speed, and maintaining consistent performance standards.