# hemansnation/AI-Engineer-Headquarters

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

Repository: https://github.com/hemansnation/AI-Engineer-Headquarters
Canonical: https://ross.abutalabs.com/products/ai-engineer-headquarters
Homepage: https://www.masterdexter.io/ai-engineer-hq
Language: Jupyter Notebook
License Family: other
Topics: python, machine-learning, deep-learning, pytorch, data-structures-and-algorithms, mlops, natural-language-processing, statistics, ai, ai-agents, aiengineering, langchain, langgraph, llm, llm-evaluation, llm-inference, llm-security, rag
Last push: 2025-11-07T16:24:38+00:00

## Health v2 (maintenance only)
Score: 55/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 51, release rhythm 35, longevity 100
- inputs: {"age_days": 1618, "days_push": 299, "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 3673, forks 699 (observed 2026-08-28T04:08:13.575017+00:00)

## What it is
An open-source, action-oriented curriculum and learning roadmap for becoming an AI engineer, covering ML, MLOps, LLMs, RAG systems, fine-tuning, and autonomous AI agents. It is organized as a structured drill with deep-work routines, video sessions, and text notes maintained in Jupyter Notebooks.

## Use cases
- learn ai engineering from scratch
- roadmap to become an llm engineer
- study machine learning and mlops with a structured curriculum
- learn to build production rag systems
- understand llm fine-tuning and evaluation
- prepare for an ai engineer career
- find a guided study plan for deep learning and nlp

## When to choose
- you want a free, structured, self-paced AI engineering curriculum
- you prefer action-oriented learning with notebooks and video sessions
- you want coverage of the full LLM stack including RAG, fine-tuning, agents, and security

## When to avoid
- you need production-ready software or a library to import into your codebase
- you want a certified or paid cohort program with mentorship and job placement
- you need a stable, versioned dependency rather than educational content

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, llm-inference, rag, agent-framework, nlp, data-science
- domain: artificial-intelligence, machine-learning, large-language-models, tutorials, education
- platform: python, cross-platform
- tags: ai-engineering, mlops, llm-fine-tuning, curriculum, langchain, langgraph, llm-evaluation, llm-security, career-development, jupyter-notebooks, retrieval-augmented-generation, ai-agents, natural-language-processing

## Member repositories
- hemansnation/AI-Engineer-Headquarters (main) score 55

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:13.575017+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 2026-08-29T18:31:17.945996+00:00, confidence not recorded.
  - readme: https://github.com/hemansnation/AI-Engineer-Headquarters (fetched 2026-08-28T04:08:13.575017+00:00, sha 74d1b7fea904)
  - homepage: https://www.masterdexter.io/ai-engineer-hq (fetched 2026-08-29T09:25:20.273662+00:00, sha e5434bd6d613)
  - site_page: https://www.masterdexter.io/about (fetched 2026-08-29T09:25:20.285121+00:00, sha 5d5589246560)
- Data as of 2026-08-30T08:39:29.467469+00:00.