[ac_alejos]

XGBoost is an extremely popular and powerful machine learning library that implemented gradient-boosted decision tree models, and is still state of the art in field such as time-series analysis and tabular data prediction while taking a fraction of the computing resources of models such as neural networks.

Elixir now supports XGBoost models (even those trained outside of Elixir) and you can leverage the benefits of Elixir concurrency and distribution to serve your decision tree models using Elixir-Nx's `Nx.Serving` construct.

In this blog post (which you can run as a Livebook), we walk through a full example of training a spam detection model using XGBoost and serving it with Elixir. Check it out!

[jaeyson]

@ac_alejos I saw your tweet regarding increasing number of curious folks with ElixirConf ML-side talks. Such a refreshing feel to know the language expands on to different fields so quick!

[ac_alejos]

Absolutely! The whole ML ecosystem has grown to where it is now in less than 3 years or so. It helps that theres a good amount of concerted effort behind it.