Learn how to deploy and serve large language models at scale using KitOps for packaging, Kubeflow for orchestration, and KServe for production-grade inference on Kubernetes.

Learn how to run scalable ML inference with Argo Workflows and KitOps ModelKits. Deploy models without rebuilding Docker images using Jozu Hub governance.

From Development to Production Running machine learning on Kubernetes has evolved from experimental curiosity to production necessity. But with hundreds of tools claiming to solve ML (machine learning) deployment, which ones should you consider?

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.