ahkarami/Deep-Learning-in-Production

Curated collection of tutorials and tools for deploying PyTorch, TensorFlow, and Keras models across multiple platforms—covering model conversion techniques (JIT tracing, ONNX, C++ APIs), serving frameworks (TorchServe, TensorFlow Serving, Flask), and deployment targets from serverless AWS Lambda to browser-based inference with TensorFlow.js. Organizes best practices for production optimization including model quantization, containerization, and thread-safe serving patterns. Bridges gap between research frameworks and production infrastructure by linking official documentation with practical implementation examples.

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