Jozu provides the backbone for scalable ML deployments.
When combined with KitOps, your models move through dev → staging → prod without breaking.
Reproducible. Portable. Secure.
We just published a new article:
“Scalable ML Deployments Made Simple with KitOps and Kubernetes (No Hardware Required)”
A deep dive into how ModelKits make ML deployments reproducible, portable, and production-ready, without complex infra setup.
Link in the Comments:
ML workflows often break at scale because model management becomes fragile:
- Config drift
- Inconsistent weights
- Mismatched data
- Hidden dependencies
Jozu + KitOps make model versioning feel more like building Docker containers: structured, reproducible, and portable.
It’s…
AI workflows are evolving!
But the way we store and share models hasn’t kept up.
We don't just need model hosting. We need:
• Version control
• Dependency pinning
• Audit logs
• Private registries
This is where Jozu Hub steps in.
The open standard for shipping AI the right way! 📦✨
KitOps is an open-source DevOps tool for
- Packaging,
- Versioning &
- Securely sharing AI/ML models, datasets & code,
All as OCI artifacts.
Perfect for security-focused teams, clouds & self-hosted AI agents.
Your ML models are part of your infrastructure.
• Versioning them like code
• Packaging them like containers
• Deploying with YAML
• Rolling back with tags
This tutorial shows exactly how to do that with Jozu + KitOps.
Why teams are choosing Jozu for AI ops 👇
- 31% faster dev cycles
- 3 days saved per model release
- Fewer security issues w/ built-in scanning
- EU AI Act–ready (signing, versioning, audits)
- Works with your DevOps tools
- OCI-based ModelKits via KitOps
Try it now!
ML models rot without good MLOps.
A proof-of-concept is cool, but a repeatable, versioned, auditable deployment is better.
Jozu’s Hugging Face import + KitOps ModelKits turn Meta’s SAM into a production-grade artifact, from dev to K8s with audit trails.
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