Pipeline that loads the Wisconsin Breast Cancer diagnostic dataset, performs preprocessing, and splits data into training and testing sets.
Pipeline that trains classification models (SGD and Random Forest) and evaluates them on test data, promoting the best performer to production.
Pipeline that uses the production model to generate predictions on new data, leveraging the same preprocessing as during training.
Pipeline that deploys the production model as a FastAPI service, making it accessible via REST API with interactive Swagger documentation.
OncoClear is an end-to-end MLOps solution that transforms raw diagnostic measurements into reliable cancer classification predictions. Built with ZenML's robust framework, it delivers enterprise-grade machine learning pipelines that can be deployed in both development and production environments.
OncoClear delivers production-ready breast cancer classification through comprehensive MLOps pipelines. It processes medical diagnostic data with automatic model versioning, comparison, and promotion workflows, ensuring only the highest-performing models reach production.
