12 MINUTE READ
Build Bulletproof ML Pipelines with Automated Model Versioning
Jesse Williams
COO/CMO
The Jozu Blog
12 min read
Audit Logging for ML Workflows with KitOps and MLflow
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.
12 min read
Instant Rollbacks On-Prem & Edge with Jozu + KitOps
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.
ai - 26 min read
Serving LLMs at Scale with KitOps, Kubeflow, and KServe
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.
11 min read
Stop Rebuilding Docker Images: Deploy ML Models at Scale with Argo and KitOps
Learn how to run scalable ML inference with Argo Workflows and KitOps ModelKits. Deploy models without rebuilding Docker images using Jozu Hub governance.
14 min read
Top Open Source Tools for Kubernetes ML: From Development to Production
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?
5 min read
