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?
The ML ecosystem has long struggled with a fundamental problem: every tool has its own packaging format. Moving models between environments meant reformatting everything. Security scanning and governance were afterthoughts. Supply chain controls didn’t exist. Today, that changes. Jozu announces two developments that address these gaps: ModelPack and KitOps are now CNCF-backed industry standards for
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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.
