TL;DR: You’re storing ML models in S3 and deploying them with Kserve. That’s fine until someone asks: “Who deployed this model? Is it secure? Can we rollback?” Then you realize you have no answers. Jozu fixes this with by adding the security and governance layer enterprises need with: Kserve model versioning Kserve security scanning Kserve
We had an incredible time at KubeCon India! Many attendees asked thoughtful questions about Jozu’s architecture, deployment options, and integration capabilities. In this post, we address the most common questions about on-premises deployment, security, workflows, and how KitOps compares to other tools.
Learn how to build an image classification model using the Satellite Image Classification dataset and package it with KitOps ModelKit for consistent deployment. We explore how proper packaging addresses common reproducibility challenges in AI/ML projects.
Learn how to streamline ML deployments using KitOps and Kubernetes. This comprehensive guide walks through packaging models into portable ModelKits and deploying them to Kubernetes clusters for scalable production environments.
Learn how Jozu Orchestrator On-Premise enables secure, self-hosted ML model management using OCI and OIDC. Explore architecture, ModelKit workflows, and deployment best practices.
Learn how to deploy Meta’s Segment Anything Model (SAM) from Hugging Face to production using Jozu’s MLOps platform. Complete guide covers importing SAM, local testing with kit-cli, and Kubernetes deployment with step-by-step instructions and code examples.
