Episode 76: I put my money on Elixir with Chris Grainger
Jul 17, 2024
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Chris Grainger discusses his journey into Elixir and using machine learning and AI to build a product. They explore Elixir's functional approach, NLP in patent analysis, and the benefits of Livebook. The chapter also touches on Elixir's integration with MLIR for machine learning on GPUs and the challenges with AI models.
Elixir's functional approach attracts data scientists for web development.
Integration of Rust with Elixir enhances performance for machine learning tasks.
Community support and tools like MLIR facilitate Elixir's growth in handling large ML models.
Deep dives
Trade Show Season and Special Guest Introduction
Trade show season is discussed with a focus on car trade shows and tools used in garages. Lars Wichmann introduces Alex Kudmas and highlights the special guest Chris Granger from amplified AI who specializes in using machine learning and AI in Elixir for building products and businesses.
Chris Granger's Background and Introduction to Elixir
Chris Granger shares his background in data science and how he transitioned to Elixir from languages like R and Python. Coming from a data science background, the functional approach of Elixir appealed to him for web development, providing a fresh perspective on building products.
The Role of Elixir in AI and Machine Learning
The discussion delves into how Elixir interfaces with C++ libraries for machine learning and the use of Rust for performance bottlenecks. The integration of Rustler allows for seamless interaction between Elixir and Rust, enhancing capabilities for data manipulation and machine learning tasks.
Community Building and Challenges in Machine Learning with Elixir
The episode highlights the importance of community growth for Elixir in machine learning, addressing challenges like quantization and the need for specialized GPU support. Collaborative efforts and advancements in tools like MLIR aim to expand Elixir's capabilities in handling larger machine learning models.
Closing Remarks and Shoutouts
The conclusion emphasizes the Erlang Ecosystem Foundation and the vibrant discussions in the machine learning channel on Slack for those interested in exploring AI and machine learning developments within the Elixir community. Listeners are encouraged to engage in building innovative solutions and contributing to the dynamic Elixir ecosystem.
Chris, Lars and Alex discuss Chris's journey into Elixir and using machine learning and AI in Elixir to build a product and a business. They also touch on the process of training models and the benefits of using Livebook.