ahkarami/Deep-Learning-in-Production resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

4376 stars · 685 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A curated collection of notes, tutorials, and reference links about deploying deep learning models in production, covering PyTorch, TensorFlow, Keras, ONNX, TorchServe, and serving via Flask/REST APIs. It is a reading list rather than runnable software.

Use cases

• learn how to deploy pytorch models to production
• serve a deep learning model as a rest api
• convert pytorch models for inference in c++
• find resources on model serving with torchserve
• deploy deep learning models on aws lambda
• understand onnx and onnx runtime for deployment

When to choose

• you want a curated reading list on deep learning deployment and model serving
• you are exploring options like TorchServe, ONNX Runtime, or Flask-based serving
• you need references spanning multiple frameworks (PyTorch, TensorFlow, Keras, MXNet)

When to avoid

• you need a ready-to-run serving framework rather than links and notes
• you expect maintained code with a license and releases
• you want step-by-step production infrastructure tooling out of the box

Facets

learning-resource · maturity active

machine-learning deep-learning llm-inference developer-tools deep-learning machine-learning developer-tools tutorials python cpp cross-platform model-serving model-deployment pytorch tensorflow curated-links mlops production-ml

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