Design a Machine Learning Model Deployment System

Functional Requirements:

• Model Deployment: Ability to deploy machine learning models efficiently and reliably.
• Version Control: Support for multiple versions of models to allow for rollback and comparison.
• Scalability: System should handle varying loads and scale horizontally as needed.
• Monitoring and Logging: Real-time monitoring of model performance and logging for debugging and auditing.
• Security: Secure access to models and data, including authentication and authorization.
• Model Retraining: Support for automated or manual retraining of models based on new data.
• API Access: Provide RESTful API endpoints for model inference.
• Data Preprocessing: Ability to preprocess input data before feeding it to the model.
• Feedback Loop: Mechanism to collect feedback on model predictions for continuous improvement.

Non-Functional Requirements:

• Performance: Low latency for model inference requests.
• Reliability: High availability and fault tolerance to ensure continuous operation.
• Maintainability: Easy to update and maintain the system with minimal downtime.
• Usability: User-friendly interface for deploying and managing models.
• Compliance: Adherence to data privacy regulations and industry standards.
• Cost Efficiency: Optimize resource usage to minimize operational costs.
• Interoperability: Compatibility with various machine learning frameworks and tools.
• Extensibility: Ability to add new features or integrate with other systems in the future.