Containerized Machine Learning Model Deployment Pipeline

Design a sophisticated Docker-based deployment pipeline for machine learning models used in educational predictive analytics. Create a multi-stage build process that supports model versioning, A/B testing deployment strategies, and automatic model retraining workflows. Implement a Kubernetes-based solution that can dynamically load and serve multiple machine learning models, with built-in support for TensorFlow and scikit-learn models, including comprehensive logging and performance tracking.