How do you ensure scalability when deploying ML models?

To ensure scalability when deploying ML models, consider these strategies: 1. Microservices Architecture: Break down the model into microservices for independent scaling. 2. Load Balancing: Distribute incoming requests across multiple model instances to handle high traffic. 3. Containerization: Use Docker or Kubernetes to manage deployment environments and scale instances on demand. 4. Auto-scaling: Implement auto-scaling mechanisms that automatically adjust resources based on demand. 5. Caching: Use model response caching to reduce load on the model for frequently requested predictions.