# The-AI-Summer/Deep-Learning-In-Production

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

Repository: https://github.com/The-AI-Summer/Deep-Learning-In-Production
Canonical: https://ross.abutalabs.com/products/the-ai-summer-deep-learning-in-production
Homepage: https://amzn.to/3oa50Vj
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, production, deep-learning, ai, tensorflow, neural-network, python, deployment, cloud, training, unet, semantic-segmentation, machinelearningproject, deeplearning
Last push: 2023-05-01T22:00:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2292, "days_push": 1220, "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 1264, forks 267 (observed 2026-08-28T04:04:10.844832+00:00)

## What it is
An open-source book (with accompanying Jupyter Notebook examples) about building, training, deploying, scaling, and maintaining deep learning models in production. It covers ML infrastructure and MLOps practices using tools like TensorFlow, Flask, Docker, Kubernetes, and Google Cloud.

## Use cases
- learn how to deploy deep learning models to production
- understand mlops and end-to-end ml pipelines
- learn best practices for writing deep learning code
- how to serve and scale machine learning models
- unit testing machine learning code
- productionize models as a data scientist
- build ml infrastructure with docker and kubernetes

## When to choose
- you are a software engineer starting out with deep learning
- you are an ML researcher with limited software engineering background
- you want hands-on examples of deploying and scaling models with TensorFlow and Kubernetes
- you want a structured end-to-end resource covering the full ML lifecycle

## When to avoid
- you need a maintained software library or framework rather than educational material
- you need coverage of PyTorch or non-TensorFlow ecosystems
- you need up-to-date MLOps tooling, as the content was last updated in 2023
- you are looking for advanced research on novel model architectures

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, deployment, testing, etl, documentation
- domain: deep-learning, machine-learning, tutorials, cloud-computing
- platform: python, cloud, cross-platform
- tags: mlops, book, jupyter-notebooks, tensorflow, model-serving, production-ml, hands-on-examples, devops, docker, kubernetes

## Member repositories
- The-AI-Summer/Deep-Learning-In-Production (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:10.844832+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 2026-08-30T05:04:02.112912+00:00, confidence not recorded.
  - readme: https://github.com/The-AI-Summer/Deep-Learning-In-Production (fetched 2026-08-28T04:04:10.844832+00:00, sha 5a314a50e432)
  - homepage: https://amzn.to/3oa50Vj (fetched 2026-08-29T12:16:17.026902+00:00, sha e1a0ab023dca)
- Data as of 2026-08-30T08:39:29.467469+00:00.