# adithya-s-k/AI-Engineering.academy

Mastering Applied AI, One Concept at a Time

Repository: https://github.com/adithya-s-k/AI-Engineering.academy
Canonical: https://ross.abutalabs.com/products/ai-engineeringacademy
Homepage: https://aiengineering.academy
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: fine-tuning, finetuning, finetuning-llms, inference, large-language-models, llm, python, quantization
Last push: 2026-02-27T19:08:42+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 69, release rhythm 35, longevity 75
- inputs: {"age_days": 1063, "days_push": 187, "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 2377, forks 277 (observed 2026-08-28T04:06:42.108800+00:00)

## What it is
AI Engineering Academy is an open-source educational resource offering structured learning paths for applied AI, covering prompt engineering, RAG, LLM fine-tuning, deployment, and AI agents. It provides hands-on Jupyter notebook projects and curated curricula for mastering production-ready AI engineering skills.

## Use cases
- learn how to fine-tune large language models
- understand RAG architecture and build retrieval-augmented systems
- study prompt engineering techniques and best practices
- learn to deploy and scale LLM applications in production
- build autonomous AI agents with tool integration
- find hands-on AI engineering projects for a portfolio

## When to choose
- you want a structured, project-based curriculum for applied AI and LLM engineering
- you prefer learning through Jupyter notebooks with real implementations
- you need curated learning paths covering the full LLM lifecycle from prompting to deployment

## When to avoid
- you need production software or a library to integrate into an application
- you want a formal accredited course with instructor support
- you are looking for non-LLM machine learning topics like classical ML or computer vision

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, llm-inference, rag, prompt-engineering, agent-framework
- domain: large-language-models, artificial-intelligence, tutorials, machine-learning
- platform: python, cross-platform
- tags: jupyter-notebooks, fine-tuning, quantization, structured-curriculum, hands-on-projects

## Member repositories
- adithya-s-k/AI-Engineering.academy (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:42.108800+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 2026-08-30T02:34:55.599585+00:00, confidence not recorded.
  - readme: https://github.com/adithya-s-k/AI-Engineering.academy (fetched 2026-08-28T04:06:42.108800+00:00, sha 2b8dceb277c8)
  - homepage: https://aiengineering.academy (fetched 2026-08-29T10:15:49.903261+00:00, sha ed1126a2abbc)
- Data as of 2026-08-30T08:39:29.467469+00:00.