🔟 How do you deploy and scale deep learning models in production?
Deployment requires model serving (using frameworks like TensorFlow Serving or TorchServe), optimizing for inference speed (quantization, pruning), monitoring performance, and infrastructure setup (GPUs, containerization with Docker/Kubernetes). Also important are model versioning, A/B testing, and strategies for rollback.

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