Hi HN, I'm one of the founders and would love to give some context. Our aim is to make building machine learning powered applications as painless as possible. We built the product that we wish existed when we were flailing about, trying to stand up ML applications in the past.
We’ve built a product that allows users to seamlessly integrate ML models with their custom business logic, expose them as APIs, and ultimately assemble user-facing apps that utilize those APIs. Many ML practitioners find dealing with infra (e.g. docker, k8s) and front-end engineering to be daunting tasks. We want to show them that they can achieve a lot more (e.g. build full-fledged ML-enabled user-facing applications) without having to learn those skill sets.
Our aim was to build something that was 1) simple, 2) powerful, and 3) a joy to use.
This is our take on that — we’d love to hear what you think
as the sibling comment mentioned, the other two founders were recently on a podcast with more details, but i can give the TLDR here: we've all worked as ML practitioners or supporting them in engineering roles. We realised there are common patterns in establishing a ML powered product, eg deployment, integrating into business logic/databases, building UI to prototype functionality and eventually turning that prototype into a functional application. We've built the blocks that allow you to put these together in novel ways without constantly reinventing the wheel and being distracted by common pitfalls.
We’ve built a product that allows users to seamlessly integrate ML models with their custom business logic, expose them as APIs, and ultimately assemble user-facing apps that utilize those APIs. Many ML practitioners find dealing with infra (e.g. docker, k8s) and front-end engineering to be daunting tasks. We want to show them that they can achieve a lot more (e.g. build full-fledged ML-enabled user-facing applications) without having to learn those skill sets.
Our aim was to build something that was 1) simple, 2) powerful, and 3) a joy to use.
This is our take on that — we’d love to hear what you think