# https-deeplearning-ai/machine-learning-engineering-for-production-public

Public repo for DeepLearning.AI MLEP Specialization

Repository: https://github.com/https-deeplearning-ai/machine-learning-engineering-for-production-public
Canonical: https://ross.abutalabs.com/products/machine-learning-engineering-for-production-public
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2024-10-28T05:04:01+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": 2116, "days_push": 674, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1972, forks 2332 (observed 2026-08-28T04:06:00.998749+00:00)

## What it is
Public repository of course materials for DeepLearning.AI's Machine Learning Engineering for Production (MLEP) Specialization. It contains Jupyter Notebook resources covering deploying ML systems in production.

## Use cases
- learn machine learning engineering for production
- study mlops course materials
- find jupyter notebooks for deploying ML models
- follow the deeplearning.ai MLEP specialization
- learn how to productionize machine learning pipelines
- self-study ML system deployment

## When to choose
- you are enrolled in or self-studying the MLEP specialization
- you want free official notebooks for ML production practices
- you prefer learning via Jupyter notebooks

## When to avoid
- you need a production-ready MLOps tool rather than course content
- you want actively maintained community-contributed materials (PRs are not accepted)
- you need beginner ML fundamentals rather than production engineering

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: machine-learning, deep-learning, education
- platform: python, cross-platform
- tags: mlops, course-materials, jupyter-notebooks, deeplearning-ai, specialization, production-ml

## Member repositories
- https-deeplearning-ai/machine-learning-engineering-for-production-public (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:00.998749+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 2026-08-30T03:05:02.145397+00:00, confidence not recorded.
  - readme: https://github.com/https-deeplearning-ai/machine-learning-engineering-for-production-public (fetched 2026-08-28T04:06:00.998749+00:00, sha 3d742815881c)
- Data as of 2026-08-30T08:39:29.467469+00:00.