> For the complete documentation index, see [llms.txt](https://matrix-neo.gitbook.io/trinity/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://matrix-neo.gitbook.io/trinity/welcome-to-trinity-neo-automl-blog.md).

# Welcome to Trinity-Neo AutoML Blog

<figure><img src="/files/IedC92HYtqd6QtX7c2h8" alt=""><figcaption></figcaption></figure>

> Author: S. Satish Kumar          &#x20;
>
> Contact Email: <sathishsriram999@gmail.com>, <sathishsriram369@gmail.com>

## Trinity-Neo 1.0

An open-source, low-code machine learning library in Python

Trinity-Neo is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes you more productive.

Compared with the other open-source machine learning libraries, Trinity-Neo is an alternate low-code library that can be used to replace hundreds of lines of code with a few lines only. This makes experiments exponentially fast and efficient. Trinity-Neo is essentially a Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, Pycaret, Multirake, NLP, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more.