How Elixir can reach the machine Learning Community - Code BEAM V America pre-event panel discussion

There has been a lot of excitement in the Erlang Ecosystem following José's announcement and demonstrations of Nx. We thought it was a great opportunity to bring the community together for a Meetup in preparation for Code BEAM V America to discuss how Elixir can reach the machine learning community.
Our panel consisted of Bruce Tate (author of 7 languages in 7 weeks), Justin Schneck (Nerves Core Team, working on integrating Elixir and Machine Learning), Brian Troutwine (Well-known Erlang developer currently using Rust for speed) and Garrett Smith (Founder of Guild AI),

[carlwatts]

You could try to become early adopters of blockchain ai projects like Fetch ai or SingularityNet and gain popularity in these circles. They may flop but then again they may become big.

Fetch ai is working among other thigns on a framework for collaborative ai where multiple organizations can train and improve a common model while keeping their data private. As far as i understand it each would take turns training the model and publishing the new weights which would get voted on before the next organization takes turn training the model.

SingularityNet has bigger ambitions but also tries to be a practical non-proporietary open market place for ai services

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[CristianGarcia]

I the mayor problem is that the machine learning ecosystem mostly cares about training and its dominated by Python. For deployment (MLOps) you can use whatever language as long as is can run SavedModels, TorchScripts, or even TensorRT compiled models. There is a ton of infrastructure around this so its not easy to compete.