# ahkarami/Deep-Learning-in-Production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

Repository: https://github.com/ahkarami/Deep-Learning-in-Production
Canonical: https://ross.abutalabs.com/products/deep-learning-in-production
License Family: other
Topics: deep-learning, deep-neural-networks, python, pytorch, tesnorflow, keras, mxnet, caffe2, production, serving, c-plus-plus, model-serving, tutorial, flask, rest-api, react, serving-pytorch-models, convert-pytorch-models, angularjs, tensorflow-models
Last push: 2024-11-09T07:57:41+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3044, "days_push": 662, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4376, forks 685 (observed 2026-08-28T04:08:46.893802+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, llm-inference, developer-tools
- domain: deep-learning, machine-learning, developer-tools, tutorials
- platform: python, cpp, cross-platform
- tags: model-serving, model-deployment, pytorch, tensorflow, curated-links, mlops, production-ml

## Member repositories
- ahkarami/Deep-Learning-in-Production (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.893802+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:19.491267+00:00, confidence not recorded.
  - readme: https://github.com/ahkarami/Deep-Learning-in-Production (fetched 2026-08-28T04:08:46.893802+00:00, sha 84bdfa20a9e4)
- Data as of 2026-08-30T08:39:29.467469+00:00.