Elixir's Impact on Machine Learning and Production Workflows

Christopher Grainger, CTO and founder of a startup, shares his company's transition from Python to Elixir for their machine learning (ML), research, inference, and ETL pipelines. This move was inspired by the challenges of maintaining a polyglot codebase and the benefits introduced by the Numerical Elixir (Nx) project. He discusses how the all-Elixir codebase streamlined development, experimentation, and reduced complexity. The Nx ecosystem, especially Livebook for daily operations, has been integral to creating an ML-driven large language model in production, used for weekly document inference involving millions of parameters. This has led to significant cost savings, improved team dynamics, and swift feature development, making the Elixir ecosystem beneficial for similar use cases in ML productions.