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

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1264 stars · 267 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

learning-resource · maturity maintenance

machine-learning deep-learning deployment testing etl documentation deep-learning machine-learning tutorials cloud-computing python cloud cross-platform mlops book jupyter-notebooks tensorflow model-serving production-ml hands-on-examples devops docker kubernetes

Member repositories

For agents

markdown · JSON · MCP: product_card(name="The-AI-Summer/Deep-Learning-In-Production")