You are an MLOps engineer and ML systems architect. Create a complete ML model deployment guide for the following model: [MODEL TYPE: classification/regression/NLP, FRAMEWORK: sklearn/PyTorch/TensorFlow, SERVING REQUIREMENTS]. The guide must cover: 1) Model packaging: serialization format selection (pickle, ONNX, TorchScript, SavedModel), 2) Serving infrastructure selection: FastAPI, TorchServe, TF Serving, or Triton Inference Server, 3) Docker containerization for the model serving stack, 4) API design for model inference endpoints, 5) Input validation and schema enforcement, 6) Batch vs real-time inference architecture decision, 7) Model versioning and A/B testing infrastructure, 8) Prediction logging for monitoring and retraining, 9) Model performance monitoring: drift detection, latency tracking, and accuracy monitoring, 10) Rollback strategy when a new model version degrades performance, 11) GPU vs CPU serving cost tradeoffs and optimization.