Your data science team has delivered a trained machine learning model in a .pkl file. Your task is to deploy this model as a scalable, reliable, and maintainable web service. Develop a comprehensive, step-by-step plan that addresses the following key areas:

1. Building a REST API: Describe the architectural considerations for wrapping the model, the choice of framework (e.g., Flask, FastAPI), how you would design API endpoints, handle input/output (e.g., data serialization/deserialization, validation), and implement robust error management.

2. Containerization with Docker: Detail the process of creating an efficient Dockerfile for the application. Explain how to build a Docker image, manage Python dependencies, and discuss best practices for containerizing a machine learning application (e.g., multi-stage builds, handling large model files).

3. Cloud Deployment: Outline a strategic approach for deploying the containerized application to a chosen cloud environment (e.g., AWS, Azure, GCP). Compare and contrast deployment options such as Kubernetes, serverless functions (e.g., AWS Lambda, Azure Functions), or virtual machines. Discuss key considerations for achieving scalability, ensuring high reliability, and implementing security measures.

4. Monitoring Model Performance: Explain how you would implement monitoring for both the model's operational performance (e.g., data drift detection, prediction accuracy tracking, inference latency) and the underlying infrastructure (e.g., resource utilization, API response times, error rates). Suggest specific tools or approaches for each type of monitoring.

5. Planning for Future Retraining and Versioning: Describe a robust strategy for model retraining, including the design of data pipelines for continuous training, the process for re-deployment of updated models, and how to manage different model versions (e.g., A/B testing, canary deployments, blue/green deployments). Integrate CI/CD principles within the context of MLOps for automated workflows.

Your response should demonstrate a strong understanding of MLOps principles and practical implementation steps for each section.