Oleh Kostromin
I am a Data Scientist primarily focused on Deep Learning and MLOps. In my spare time I contribute to several open-source python libraries.
Session
What if you could run real data/ML workflows right in your browsers - sandboxed, with no installation or sending your data anywhere? Such an approach would have tons of benefits: it is easy to distribute, safer by default, and can scale almost infinitely with virtually no infrastructure costs.
This talk is a pragmatic overview of the current in-browser ML stack. We’ll cover what workflows are realistic today (from training of traditional ML models to on-device LLM inference), how packaging/loading works, and the constraints one should be aware of. By the end of the talk you will have a clear sense of when in-browser ML is a good fit, and when it isn’t.