Train, test, serialize, and containerize your application using one simple command - from your CLI or your existing workflow orchestration tools
Push changes of all relevant types — including new features, configurations, models, data changes — into production in a safe and quick manner.
Using just a simple decorator in your code, automatically record every prediction made to your prediction API.
Track model health metrics such as throughput, latency, error rate and more. Configure alerts on top of them in a single
Create a single, curated, discoverable source of truth for ML features. Allow data scientists and engineers to collaborate and share features between projects and models. Ensure features are served in production the same way they were used in training.