What are some common strategies for serving ML models in production?

Common strategies for serving ML models in production include: 1. **REST API**: Exposing the model through a RESTful service allows real-time predictions. 2. **gRPC**: For low-latency and high-throughput needs, gRPC can be used over traditional HTTP services. 3. **Batch Processing**: For use cases where immediate responses aren't necessary, models can handle batch requests periodically. 4. **Edge Deployment**: Deploying models on edge devices for reduced latency. 5. **Model as a Service (MaaS)**: Using cloud platforms or SaaS solutions to handle the deployment and scaling of models. 6. **Microservices Architecture**: Breaking down the model serving process into microservices for better scalability and maintenance.