# chris-chris/ml-engineer-roadmap

WIP: Roadmap to becoming a machine learning engineer in 2020

Repository: https://github.com/chris-chris/ml-engineer-roadmap
Canonical: https://ross.abutalabs.com/products/ml-engineer-roadmap
License Family: other
Last push: 2021-09-16T17:04:31+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": 2323, "days_push": 1812, "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 2206, forks 252 (observed 2026-08-28T04:06:26.001568+00:00)

## What it is
A visual roadmap of charts outlining the skills and technologies needed to become a machine learning engineer, inspired by the web developer roadmap. It is a documentation-style guide rather than runnable software, with editable Balsamiq project files for contributions.

## Use cases
- plan a path to become a machine learning engineer
- figure out what to learn next in ML
- get an overview of the ML engineering landscape
- guide college students studying machine learning
- compare tools and technologies for ML careers

## When to choose
- you want a high-level visual curriculum for ML engineering
- you are a beginner deciding what to study first
- you mentor others and need a shareable learning path

## When to avoid
- you need hands-on tutorials or code examples
- you want up-to-date 2024+ tooling recommendations
- you need an interactive or structured course

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: machine-learning, tutorials, education
- platform: cross-platform
- tags: roadmap, career-guide, machine-learning-engineer, learning-path, charts

## Member repositories
- chris-chris/ml-engineer-roadmap (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:26.001568+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 2026-08-30T02:46:29.637284+00:00, confidence not recorded.
  - readme: https://github.com/chris-chris/ml-engineer-roadmap (fetched 2026-08-28T04:06:26.001568+00:00, sha 8f42cb8e347f)
- Data as of 2026-08-30T08:39:29.467469+00:00.