From Python to Elixir Machine Learning

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From Python to Elixir Machine Learning
The author, Andrés Alejos, details the process of transitioning from machine learning in Python to utilizing the growing ecosystem of machine learning libraries in Elixir. He posits that the concurrency support and distributed system capabilities of Elixir make it suitable for ML applications, despite Python's historical dominance in the field. Andrés shares his experience in porting two libraries, EXGBoost and Mockingjay, originally implemented in Python, to Elixir counterparts, highlighting Elixir-Nx's 'Nx.Serving' as a viable distributed model serving solution. He provides an overview of his workflow in the porting process, including understanding the macro system, reading documentation, studying source code in detail, and finally, implementing new code. Through examples, Andrés shows the transition of code idioms from Python's object-oriented approach to Elixir's functional and pattern-based paradigms, demonstrating Elixir's capabilities with the Nx library for tensor computations and strategies like GEMM, Tree Traversal, and Perfect Tree Traversal. He concludes by encouraging Elixir developers to take on the challenge of bringing machine learning into their ecosystem, citing its potential and the support provided by the community.

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