# alirezadir/Production-Level-Deep-Learning

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

Repository: https://github.com/alirezadir/Production-Level-Deep-Learning
Canonical: https://ross.abutalabs.com/products/production-level-deep-learning
License Family: other
Topics: machine-learning, deep-learning, pipeline, scalable-applications, production-system, tfx, kubeflow, artificial-intelligence, ai, practical-machine-learning, deployment, system-design
Last push: 2025-06-13T01:52:26+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 26, release rhythm 35, longevity 100
- inputs: {"age_days": 2479, "days_push": 447, "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 4659, forks 685 (observed 2026-08-28T04:08:56.413765+00:00)

## What it is
A curated engineering guideline for building and deploying production-level deep learning systems, covering the full ML project lifecycle from scoping to deployment. It aggregates material from Full Stack Deep Learning, TFX workshops, and Kubeflow meetups.

## Use cases
- learn how to deploy deep learning models to production
- understand the machine learning project lifecycle
- design a scalable ML pipeline with TFX or Kubeflow
- prepare for MLOps or ML system design interviews
- avoid common reasons AI projects fail in production
- find best practices for productionizing ML systems

## When to choose
- you want a structured overview of taking deep learning models from research to production
- you are learning MLOps concepts like pipelines, deployment, and system design
- you need a reference covering the full ML project lifecycle

## When to avoid
- you need runnable production code or a maintained framework rather than a guide
- you want tooling with an active release and license for direct integration
- you need hands-on tutorials with code exercises

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, deployment, workflow-automation
- domain: deep-learning, machine-learning, tutorials
- platform: cloud, python
- tags: production-ml, mlops, system-design, guideline, tfx, kubeflow, model-deployment, devops, docker, kubernetes

## Member repositories
- alirezadir/Production-Level-Deep-Learning (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:56.413765+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 2026-08-29T18:19:27.386763+00:00, confidence not recorded.
  - readme: https://github.com/alirezadir/Production-Level-Deep-Learning (fetched 2026-08-28T04:08:56.413765+00:00, sha 4135d4f58f49)
- Data as of 2026-08-30T08:39:29.467469+00:00.