Learn how to build automated compliance gates for AI deployments using KitOps, Jozu Hub, and OPA. This tutorial walks through packaging, scanning, policy enforcement, and cryptographic attestation so every model in production can prove it belongs there.
Learn how to build an end-to-end ML audit trail using MLflow for experiment tracking, KitOps for model packaging, and Jozu for centralized governance and visibility.
Learn how to implement instant ML model rollbacks using KitOps ModelKits. This guide covers three playbooks for Kubernetes, GitOps, and edge deployments that turn rollbacks into simple tag flips—reducing MTTR, limiting blast radius, and eliminating the need for image rebuilds.
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?
