Hi HN! We are Isabella and David, and we're excited to share Tangram, our attempt to make ML easy for programmers who are not experts. With Tangram, you train a model from a CSV file on the command line, use your model from one of many languages (so far we have libraries for Elixir, Go, JavaScript, Python, Ruby, and Rust), and learn about your models and monitor them in production from a web app. There's a video on our homepage (<a href="https://www.tangram.dev" rel="nofollow">https://www.tangram.dev</a>) and we're on GitHub at <a href="https://github.com/tangramdotdev/tangram" rel="nofollow">https://github.com/tangramdotdev/tangram</a>.<p>Over the past few months we have been working with a handful of early users. A team at a small company had a TensorFlow model deployed as a Flask service consumed by their Elixir app. They replaced it with a Tangram model because they didn't want to maintain a server separate from their monolith. A team of front end engineers at a large company was looking for a way to to train and deploy models on their own, without the overhead of involving their data scientists, machine learning engineers, or backend engineers. They trained a model on their own and embedded it directly in their React front-end with the Tangram JavaScript library that makes predictions with WebAssembly.<p>Tangram is written entirely in Rust, from the core machine learning algorithms, to the bindings for each language, to the front and back end of the web application. We have benefited from Rust's fast performance, strong typing, convenient tooling, and high quality libraries (serde, tokio, hyper, sqlx, and more).<p>We hope to make Tangram a sustainable business with the open core business model. The CLI and language libraries are MIT licensed, while the web application is source available, free to use for testing, but requires a paid license to use in production.<p>We would love to hear your feedback. Give it a try and let us know what you think!