Building Scalable Machine Learning Applications with Elixir

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Building Scalable Machine Learning Applications with Elixir
Sean Moriarity, a Machine Learning Advisor, explains the approachable and practical aspect of developing machine-learning applications using the Elixir programming language. The article showcases the creation of an enriched newsfeed application that processes headlines in real-time with machine learning enhancements like automatic retraining and data labeling tools. Moriarity highlights the simplicity of Elixir and its powerful language primitives, which often negate the need for extensive libraries found in other ecosystems like Python. He then outlines the process of creating a Phoenix application and integrating the necessary tools, such as the Top-Headlines API, Req, Nx, EXLA, and Bumblebee, to interact with machine-learning models. Examples include named-entity recognition and sentiment analysis enrichments. The author illustrates how to build concurrent enrichments within a LiveView, allowing for asynchronous processing that doesn't block the UI rendering. The application locks into a simulated real-time stream using GenServer and displays continuously updated and enriched articles using LiveView. The potential for scaling such applications with Broadway and combining them with job processors for continuous learning is briefly discussed, highlighting the flexibility and scalability of machinery learning applications in Elixir.

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