Elixir - From Python to Elixir Machine Learning

Moving on from Python Machine Learning might seem impossible. Let me break down why and how you can do it. As Elixir's Machine Learning (ML) ecosystem grows, many Elixir enthusiasts who wish to adopt the new machine learning libraries in their projects are stuck at a crossroads of wanting to move away from their existing ML stack (typically Python) while not having a clear path of how to do so.

The article shows that Python has long been the gold standard in the realm of machine learning, but it does have limitations when it comes to speed and support for concurrency. However, the Elixir community has been making significant efforts to develop state-of-the-art machine learning libraries that can compete with Python.

One such library is Elixir-Nx, which aims to provide an alternative option for integrating machine learning into applications. Nx.Serving, a component of Elixir-Nx, offers a drop-in solution for serving distributed machine-learning models. This takes advantage of the native distributed support of Elixir and the BEAM VM.

The author, Sean Moriarity, suggests that now is a good time to start porting machine learning code from Python to Elixir. He shares his experience of porting two libraries, EXGBoost (from Python XGBoost) and Mockingjay (from Python Hummingbird), and provides insights into the process.

For developers interested in exploring Elixir's machine learning capabilities, this article offers valuable information and guidance on how to proceed.