Our mission is to help data teams build processes for continual improvement. That means rapidly pushing updates of all types - new features, experiments, model updates and retraining - into production safely and quickly in a sustainable way. As the machine learning market matures, tooling that supports this area is key to helping more teams scale.
We achieve all this by abstracting ML code from underlying compute and data infrastructure, making it easier to push updates across all stages of a development lifecycle without requiring constant manual changes for optimization, testing, and deployment. Rather than focusing all efforts on the overhead of getting ML code into production, teams can refocus on their business logic so that they can achieve higher output per contributor and overall happier teams.
Our team comes from deep experience in machine learning and the startup ecosystem. We've managed large teams of ML engineers, built big data infrastructure for the fortune 500, and have helped scale hyper-growth startups.