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System Design Question

Design a Machine Learning Model Deployment System

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  1. 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.
  2. 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.

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