MLOps

Learn how to combine Weights & Biases experiment tracking with KitOps ModelKits for reproducible ML workflows. This tutorial shows you how to train, package, and deploy models to production with full lineage tracking, automatic SBOM generation, and security scanning—eliminating the ‘works on my machine’ problem for ML deployments.

Learn how to transform your ML training notebooks into deployable ModelKits using KitOps and Marimo. This comprehensive tutorial covers packaging your machine learning models with all dependencies, datasets, and code into a single, shareable artifact for seamless deployment.

Learn how to use Flux CD and KitOps together to create repeatable, shareable, and scalable ML deployment workflows using GitOps principles for production-ready AI/ML applications.