Exploring Advanced Machine Learning with Elixir’s FLAME and Nx Libraries

Sean Moriarity’s article sheds light on the FLAME library, recently released by Chris McCord and the Phoenix team, which aims to revolutionize the handling of elastic workloads in a more efficient and cost-effective manner compared to serverless solutions. FLAME enables modular component execution on short-lived infrastructure, facilitated by the BEAM and capable of replacing serverless workflows, especially in machine learning applications. The article also explores the use of FLAME in conjunction with Nx, an Elixir numerical computation library, for both online (real-time) and offline (batch) machine learning inference workloads. FLAME's approach to elastic workloads allows for embedding these workloads into applications without the complexity and cost associated with serverless functions. Moriarity delves into the practical implementation of FLAME and Nx for building inference pipelines and how FLAME can also be leveraged for training machine learning models within the application, streamlining the machine learning workflow from training to production.