62 posts in MLOps
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 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?
Learn how to combine Weights & Biases experiment tracking with KitOps ModelKits for reproducible ML workflows. This tutorial shows you how to train, package, and deploy models to production with full lineage tracking, automatic SBOM generation, and security scanning—eliminating the ‘works on my machine’ problem for ML deployments.
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.
