Project Overview
This project demonstrates a production-grade MLOps pipeline leveraging cloud-native orchestration (Kubernetes) and automated tracking/deployment (MLflow). The workflow eliminates manual handoffs by coordinating code changes, data validation, training, and automated deployment of models to a managed cloud cluster.
Key Outcomes
- Reduced operational toil for ML engineers and data scientists.
- Enabled repeatable, version-controlled model deployment using best practices in CI/CD and infrastructure-as-code.
- Achieved robust monitoring and rollback mechanisms to ensure system reliability.
What I Did
- Led design and end-to-end implementation of ML pipeline and infra.
- Authored all supporting documentation and CI/CD templates.
- Performed training and onboarding for stakeholders.
Additional Resources