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# Jozu

On-Prem AI Model Registry & Secure ML Deployment Platform

## Sitemaps

- [XML Sitemap](https://jozu.com/sitemap.xml): Contains all public & indexable URLs for this website.

## Posts

- [Package the Agent, Not Just the Model: Native Skills Support in KitOps](https://jozu.com/blog/kitops-native-agent-skills-support-modelkits/) - Package agent skills, configs, and model weights as versioned ModelKits with KitOps v1.12.0. Push to Jozu Hub and serve locally with Jozu Rapid Inference Containers.
- [Business Wire – Jozu Assessed Awardable for Department of Defense Work in the P1 Solutions Marketplace](https://jozu.com/blog/business-wire-jozu-assessed-awardable-for-department-of-defense-work-in-the-p1-solutions-marketplace/) - Jozu has been assessed as Awardable in the U.S. Air Force's Platform One (P1) Solutions Marketplace, enabling streamlined acquisition by Department of Defense customers across connected, on-premises, and air-gapped environments.
- [Running a Local Coding Agent with OpenCode and Jozu Rapid Inference Container (RICs)](https://jozu.com/blog/running-a-local-coding-agent-with-opencode-and-jozu-rapid-inference-container-rics/) - Learn how to package a quantized LLM as a ModelKit, deploy it locally with a Jozu Rapid Inference Container, and connect it to OpenCode to run a fully private AI coding agent on your own hardware.
- [Claude Managed Agents: What It Solves and What Enterprises Still Need](https://jozu.com/blog/claude-managed-agents-what-it-solves-and-what-enterprises-still-need/) - Anthropic launched Claude Managed Agents on April 8, 2026. It addresses one of the biggest barriers to enterprise agent adoption: the infrastructure overhead required to execute complex and long-running Claude agents in production. This post breaks down what Claude Managed Agents provides, what problems it solves well, and where enterprises still have governance gaps that
- [Deploy LLMs On-Prem: From Docker Model Runner to Kubernetes with Jozu Hub](https://jozu.com/blog/deploy-llms-on-prem-docker-model-runner-kubernetes-jozu-hub/) - Learn how to extract a model from Docker Model Runner, package it as a versioned ModelKit with KitOps, push to Jozu Hub, and deploy to Kubernetes using auto-generated Rapid Inference Containers — with full governance and audit trails.
- [Audit Logging for ML Workflows with KitOps and MLflow](https://jozu.com/blog/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.
- [Stop Rebuilding Docker Images: Deploy ML Models at Scale with Argo and KitOps](https://jozu.com/blog/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.
- [Top Open Source Tools for Kubernetes ML: From Development to Production](https://jozu.com/blog/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?
- [Building an End-to-End Image Classification Pipeline with KitOps & Jozu](https://jozu.com/blog/building-an-end-to-end-image-classification-pipeline-with-kitops-jozu/) - 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.
- [How to Deploy ML Models Like Code: A Practical Guide to KitOps and Flux CD](https://jozu.com/blog/how-to-deploy-ml-models-like-code-a-practical-guide-to-kitops-and-flux-cd/) - 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.
- [Prompt Drift Is the New Shadow Deploy](https://jozu.com/blog/prompt-drift-is-the-new-shadow-deploy/) - Your model didn't change. Your prompt did. Can you prove exactly what ran in production? KitOps v1.11 treats prompts as first-class release artifacts, closing the supply-chain gap between behavior changes and governed deployments.
- [Automated AI Compliance Gates: From Training Data to Production with Cryptographic Proof](https://jozu.com/blog/automated-ai-compliance-gates-from-training-data-to-production-with-cryptographic-proof/) - 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.
- [How to Extend Your DevOps Pipeline to MLOps with KitOps](https://jozu.com/blog/turn-devops-to-mlops-pipelines-with-this-open-source-tool/) - Learn how to unify DevOps and MLOps pipelines with an open-source tool, reducing costs and complexity in ML model deployment and operations.*
- [+10 MLOps Tools for EU AI Act Compliance (2026 Guide)](https://jozu.com/blog/10-mlops-tools-that-comply-with-the-eu-ai-act/) - Stay compliant with EU AI Act requirements using these +10 MLOps tools for model transparency, risk assessment, audit trails, and governance. Updated for 2026.
- [How to Build an MLOps Pipeline: Step-by-Step Guide (2026)](https://jozu.com/blog/a-step-by-step-guide-to-building-an-mlops-pipeline/) - Build your first MLOps pipeline from scratch — data ingestion, model training, CI/CD, deployment, and monitoring. Includes tool recommendations and code examples.
- [20 Open-Source AI Tools for Building & Deploying ML Projects (2026)](https://jozu.com/blog/20-open-source-tools-i-recommend-to-build-share-and-run-ai-projects/) - The top 20 open-source tools for AI model deployment, versioning, and orchestration — covering every stage from training to production.
- [10 Best Open-Source MLOps Pipeline Tools (2026) | Free & Proven](https://jozu.com/blog/10-open-source-tools-for-building-mlops-pipelines/) - Build a production-ready MLOps pipeline with these 10 free, open-source tools — from experiment tracking to model deployment and monitoring.
- [AIOps vs DevOps vs MLOps vs LLMOps: Key Differences (2026)](https://jozu.com/blog/aiops-devops-mlops-llmops-whats-the-difference/) - What separates AIOps, DevOps, MLOps, and LLMOps — and which one do you actually need? Clear breakdown of scope, tools, and when to use each framework.
- [Business Wire – Jozu Assessed Awardable for Department of War Work in the CDAO's Tradewinds Solutions Marketplace](https://jozu.com/blog/business-wire-jozu-assessed-awardable-for-department-of-war-work-in-the-cdaos-tradewinds-solutions-marketplace/) - Jozu has achieved Awardable status through the Department of War Chief Digital and Artificial Intelligence Office's (CDAO) Tradewinds Solutions Marketplace, meaning its AI security and governance solution has met rigorous evaluation standards and is ready for rapid acquisition by DoW customers. The Tradewinds panel highlighted Jozu's capabilities in secure packaging, cryptographic signing, AI-powered security scanning,
- [TechNewsWorld – AI Rapidly Rendering Cyber Defenses Obsolete](https://jozu.com/blog/technewsworld-ai-rapidly-rendering-cyber-defenses-obsolete/) - Zscaler's ThreatLabz 2026 AI Security Report reveals that rapid enterprise AI adoption is outpacing organizations' ability to secure their systems, with AI-enabled attacks now moving at machine speed. Brad Micklea, CEO of Jozu, notes that most enterprises treat AI security as an extension of application security, but the attack surface is fundamentally different—models aren't code,
- [Instant Rollbacks On-Prem & Edge with Jozu + KitOps](https://jozu.com/blog/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.
- [Serving LLMs at Scale with KitOps, Kubeflow, and KServe](https://jozu.com/blog/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.
- [Jozu Launches Enterprise Support for CNCF-Backed ModelPack and KitOps Standards](https://jozu.com/blog/jozu-launches-enterprise-support-for-cncf-backed-modelpack-and-kitops-standards/) - The ML ecosystem has long struggled with a fundamental problem: every tool has its own packaging format. Moving models between environments meant reformatting everything. Security scanning and governance were afterthoughts. Supply chain controls didn't exist. Today, that changes. Jozu announces two developments that address these gaps: ModelPack and KitOps are now CNCF-backed industry standards for
- [Computer Weekly – Jozu drives open source ModelPack & KitOps Modelkit](https://jozu.com/blog/computer-weekly-jozu-drives-open-source-modelpack-kitops-modelkit/) - DevSecOps company Jozu is taking a prominent role in two cloud-native AI projects: KitOps and ModelPack. KitOps packages AI/ML models, datasets, code, and configurations into reproducible ModelKits that work with existing container registries and Kubernetes infrastructure. ModelPack extends the OCI standard to support large AI artifacts, enabling teams to move models between development and production
- [How KitOps and Weights & Biases Work Together for Reliable Model Versioning](https://jozu.com/blog/how-kitops-and-weights-biases-work-together-for-reliable-model-versioning/) - 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. -
- [How to Turn ML Training Notebook into Deployable ModelKits with KitOps and Marimo](https://jozu.com/blog/ml-training-notebook-deployable-modelkits-kitops-marimo/) - 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.
- [Brew Markets – AI Spending Bubble: OpenAI, Nvidia, Alibaba Pour Billions Into Infrastructure](https://jozu.com/blog/brew-markets-ai-spending-bubble-openai-nvidia-alibaba-pour-billions-into-infrastructure/) - OpenAI unveils $1 trillion datacenter plans while Alibaba commits $53B to AI infrastructure. Jozu CEO Brad Micklea suggests market turbulence will be manageable due to real use cases and revenue.
- [CIO – Doomprompting: Endless tinkering with AI outputs can cripple IT results](https://jozu.com/blog/cio-doomprompting-endless-tinkering-with-ai-outputs-can-cripple-it-results/) - A new phenomenon called 'doomprompting' – the endless refinement of AI outputs – can lead to significant organizational costs. Brad Micklea from Jozu emphasizes the importance of setting clear expectations and guardrails for AI projects.
- [What's Wrong with Your Kserve Setup (and How to Fix It)](https://jozu.com/blog/whats-wrong-with-your-kserve-setup-and-how-to-fix-it/) - 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
- [Frequently Asked Questions: A Deep Dive into Jozu and KitOps](https://jozu.com/blog/frequently-asked-questions-a-deep-dive-into-jozu-and-kitops/) - 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.
- [Scalable ML Deployments Made Simple with KitOps and Kubernetes (No Hardware Required)](https://jozu.com/blog/scalable-ml-deployments-made-simple-with-kitops-and-kubernetes-no-hardware-required/) - 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.
- [How to Tune and Deploy Your First Small Language Model (SLM)](https://jozu.com/blog/how-to-tune-and-deploy-your-first-small-language-model-sllm/) - Learn how to fine-tune and deploy your first Small Language Model (sLLM) using KitOps Dev Mode.
- [SecurityWeek–Managing the Trust-Risk Equation in AI: Predicting Hallucinations Before They Strike](https://jozu.com/blog/managing-the-trust-risk-equation-in-ai-predicting-hallucinations-before-they-strike/) - Physics-based research suggests LLMs could predict hallucinations before they occur, offering game-changing implications for AI security in high-stakes industries.
- [IT Business Net–The Hidden Risk in Your AI Stack (and the Tool You Already Have to Fix It)](https://jozu.com/blog/the-hidden-risk-in-your-ai-stack-and-the-tool-you-already-have-to-fix-it/) - Study shows 60% of organizations face costly AI model rollback issues averaging $400K. Jozu CEO explains how OCI Artifacts solve production deployment risks using existing container infrastructure.
- [Hackread–Amazon Q AI Assistant Compromised by Hacker Injecting Data-Wiping Commands](https://jozu.com/blog/amazon-q-ai-assistant-compromised-by-hacker-injecting-data-wiping-commands/) - Hacker injects malicious data-wiping commands into Amazon's Q AI coding assistant through GitHub pull request. Jozu releases PromptKit to prevent AI security vulnerabilities with auditable prompt management.
- [Techstrong.ai–OpenAI Releases Open-Weight AI Models to Compete with DeepSeek, Meta, and Mistral](https://jozu.com/blog/openai-releases-open-weight-ai-models-to-compete-with-deepseek-meta-and-mistral/) - OpenAI launches open-weight AI models gpt-oss-120b and gpt-oss-20b to compete with DeepSeek, Meta, and Mistral, offering developers customizable AI solutions for personal and cloud deployment.
- [KitOps Featured in The New Stack: Bridging DevOps and MLOps Pipelines](https://jozu.com/blog/kitops-featured-in-the-new-stack-bridging-devops-and-mlops-pipelines/) - The New Stack highlights KitOps's innovative approach to unifying DevOps and MLOps workflows through ModelKits, enabling seamless AI/ML model deployment using existing containerization tools.
- [Maintaining ML Model Accuracy With Automated Drift Detection](https://jozu.com/blog/maintaining-ml-model-accuracy-with-automated-drift-detection/) - DZone published a comprehensive tutorial on detecting and managing data drift in ML systems using KitOps, highlighting automated model retraining to ensure ML models stay accurate in production.
- [KitOps 1.0 Release—Proven in Production and Looking to CNCF](https://jozu.com/blog/kitops-1-0-release-proven-in-production-and-looking-to-cncf/) - KitOps reaches 1.0 milestone with 45,000+ installs and production deployments. Jozu submits AI packaging project to CNCF Sandbox with new Hugging Face import features.
- [Jozu Raises $4 Million Seed Round to Scale Enterprise AI Orchestration Platform](https://jozu.com/blog/jozu-raises-4-million-seed-round-to-scale-enterprise-ai-orchestration-platform-2/) - Jozu raises $4M seed funding led by HalfCourt Capital to expand enterprise AI orchestration platform. KitOps open-source project surpasses 85,000 downloads worldwide.
- [Deploying Jozu On-Premise: Architecture & Workflow Overview](https://jozu.com/blog/deploying-jozu-on-premise-architecture-workflow-overview/) - 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.
- [From Hugging Face to Production: Deploying Segment Anything (SAM) with Jozu's Model Import Feature](https://jozu.com/blog/from-hugging-face-to-production-deploying-segment-anything-sam-with-jozus-model-import-feature/) - 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.
- [Managing and Deploying Multiple Model Versions with Jozu and KitOps: A Smarter Way to Scale ML Workflows](https://jozu.com/blog/managing-and-deploying-multiple-model-versions-with-jozu-and-kitops-a-smarter-way-to-scale-ml-workflows/) - Learn how to manage and deploy multiple ML model versions using Jozu and KitOps. This step-by-step tutorial shows you how to package YOLOv5 models as versioned ModelKits, push them to registries, and deploy on Kubernetes with simple YAML configs. Perfect for teams scaling ML workflows.
- [How to Generate an AI SBOM, and What Tools to Use](https://jozu.com/blog/how-to-generate-an-ai-sbom-and-what-tools-to-use/) - AI SBOMs are critical for tracking dependencies, securing AI models, and preventing supply chain attacks. These are the best tools to simplify the process.
- [Build Bulletproof ML Pipelines with Automated Model Versioning](https://jozu.com/blog/build-bulletproof-ml-pipelines-with-automated-model-versioning/) - Reproducibility is a major blocker in ML workflows. This tutorial shows how to fix it with version-controlled model packaging and automated rollbacks.
- [Streamlining ML Workflows: Integrating KitOps and Amazon SageMaker](https://jozu.com/blog/streamlining-ml-workflows-integrating-kitops-and-amazon-sagemaker/) - A practical guide to combining model packaging and cloud-based ML tools for efficient machine learning operations.
- [Migrating From DVC to KitOps](https://jozu.com/blog/migrating-from-dvc-to-kitops/) - A technical comparison of DVC and KitOps, with implementation steps for transitioning ML projects to a containerized workflow.
- [Jozu Hub–Your private, on-prem Hugging Face registry](https://jozu.com/blog/jozu-hub-your-private-on-prem-hugging-face-registry/) - A practical guide to managing Hugging Face models with KitOps and Jozu Hub for improved workflow security and deployment.
- [Automating ML Pipeline with ModelKits + GitHub Actions](https://jozu.com/blog/automating-ml-pipeline-with-modelkits-github-actions/) - Learn how ModelKits + GitHub Actions simplify ML pipeline automation.
- [Introducing Jozu Orchestrator On-Premise](https://jozu.com/blog/introducing-jozu-orchestrator-on-premise/) - Jozu announces availability of on-premises AI orchestration platform.
- [Advanced LLM Security Best Practices You Must Know](https://jozu.com/blog/advanced-llm-security-best-practices-you-must-know/) - Hackers evolve, so should your defenses. Upgrade your LLM security with industry-best strategies.
- [25 Open Source AI Tools to Cut Your Development Time in Half](https://jozu.com/blog/25-open-source-ai-tools-to-cut-your-development-time-in-half/) - Discover 25 open-source tools to streamline your AI projects from development to production.
- [The transitory nature of MLOps: Advocating for DevOps/MLOps coalescence](https://jozu.com/blog/the-transitory-nature-of-mlops-advocating-for-devops-mlops-coalescence/) - AI/ML is a wildfire of a trend. It’s being integrated into just about every application you can think of. When compared to other technical innovations over the past years, like the blockchain, the infrastructure and tooling was being built (and still is) in parallel to discovering a meaningful application.
- [Introducing the New GitHub Action for using Kit CLI on MLOps pipelines](https://jozu.com/blog/introducing-the-new-github-action-for-using-kit-cli-on-mlops-pipelines/) - Our latest contribution to KitOps is a GitHub Action that simplifies integrating Kit CLI into your existing CI/CD toolset.
- [Why enterprise AI projects are moving too slowly](https://jozu.com/blog/why-enterprise-ai-projects-are-moving-too-slowly/) - In AI projects the biggest source of friction are the handoffs between data scientists, application developers, testers, and infrastructure engineers as the project moves from development to production.
- [Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps](https://jozu.com/blog/fine-tune-your-first-large-language-model-llm-with-lora-llama-cpp-and-kitops-in-5-easy-steps/) - Dive into the world of large language models with our step-by-step tutorial on fine-tuning using LoRA, powered by tools like llama.cpp and KitOps. LoRA (Low-Rank Adaptation) is an efficient technique for adapting pre-trained models, minimizing computational overhead. We'll guide you through setting up your environment, creating a Kitfile, building a LoRA adapter, and deploying your fine-tuned model. By the end, you'll have a packaged model ready for deployment.
- [KitOps: The Bridge Between AI/ML Models and DevOps](https://jozu.com/blog/kitops-the-bridge-between-ai-ml-models-and-devops/) - Discussing the current state of the KitOps project, where the project is headed, and some of our early ideas for productizing and releasing Jozu.
- [When to Dockerize vs. When to use ModelKit](https://jozu.com/blog/when-to-dockerize-vs-when-to-use-modelkit/) - ML development can often be a cumbersome and iterative process, with many open source tools, built to handle specific parts of the machine learning workflow. In this post we explore when to use Docker and when to use ModelKits.
- [Accelerating into AI: Lessons from AWS](https://jozu.com/blog/accelerating-into-ai-lessons-from-aws/) - Learn why competitive advantages in the post-AI world will come from creating an internal AI team, and how to sequence their projects for impact and safety.
- [How to turn a Jupyter Notebook into a deployable artifact](https://jozu.com/blog/how-to-turn-a-jupyter-notebook-into-a-deployable-artifact/) - From Jupyter Notebook to production-ready artifact: explore our guide to using KitOps and ModelKit for seamless deployment.
- [Tools to ease collaboration between data scientists and application developers](https://jozu.com/blog/tools-to-ease-collaboration-between-data-scientists-and-application-developers/) - What makes a great workflow between data scientists and ML application developers? This post discusses the disparities, required tools, and, eventually, a solution to poor collaboration.
- [Announcing the Preview Release for Jozu Hub](https://jozu.com/blog/announcing-the-preview-release-for-jozu-hub/) - We're excited to announce that the Jozu Hub is now available as an early preview.
- [10 Open Source MLOps Projects You Didn’t Know About](https://jozu.com/blog/10-open-source-mlops-projects-you-didnt-know-about/) - Discover 10 underrated open source MLOps projects to boost your machine learning workflows.
- [Secure Your AI Project With Model Attestation and Software Bill of Materials (SBOMs)](https://jozu.com/blog/secure-your-ai-project-with-model-attestation-and-software-bill-of-materials-sboms/) - This post explores multiple tools to help you secure your AI project through model attestation and Software Bill of Materials (SBOMs)
- [From Jupyter Notebook to deployed application in 4 steps](https://jozu.com/blog/from-jupyter-notebook-to-deployed-application-in-4-steps/) - Deploy your Jupyter Notebook effortlessly with a ModelKit. Learn the 4-step process from unpacking and fine-tuning to pushing and deploying your ML models.
- [Turn Your Existing DevOps Pipeline Into an MLOps Pipeline With ModelKits](https://jozu.com/blog/turn-your-existing-devops-pipeline-into-an-mlops-pipeline-with-modelkits/) - In today’s world, where almost every company is embracing artificial intelligence (AI) and machine learning (ML) into their software offering, maintaining two separate pipelines for ML-powered software systems and conventional software projects can pose challenges. This also introduces friction within the team, which can slow down the development and deployment process.
- [Critical LLM Security Risks and Best Practices for Teams](https://jozu.com/blog/critical-llm-security-risks-and-best-practices-for-teams/) - Protect your LLMs from data breaches and attacks. Explore the critical security risks and strategies to protect your models and sensitive data.
- [From Proprietary Data to Expert AI with Lamini and KitOps](https://jozu.com/blog/from-proprietary-data-to-expert-ai-with-lamini-and-kitops/) - Build AI tailored to your data. Fine-tune LLMs with Lamini and deploy securely using KitOps for maximum privacy and ease of integration.
- [Top 5 Production-Ready Open Source AI Libraries for Engineering Teams](https://jozu.com/blog/top-5-production-ready-open-source-ai-libraries-for-engineering-teams/) - Discover the top 5 production-ready open source AI libraries like PyTorch, HuggingFace, and ModelKits, empowering engineering teams to build and deploy production-ready AI models
- [Free Online Tutorials to Help You Develop Machine Learning Applications](https://jozu.com/blog/free-online-tutorials-to-help-you-develop-machine-learning-applications/) - Learn machine learning with 10 free online tutorials that guide you from foundational math and data science to building real-world ML applications.
- [Building an MLOps pipeline with Dagger.io and KitOps](https://jozu.com/blog/building-an-mlops-pipeline-with-dagger-io-and-kitops/) - Learn how to build scalable MLOps pipelines with Dagger.io and KitOps. Streamline model deployment, monitoring, CI/CD, and version control for faster ML production.
- [Top Threats for AI/ML Development and How to Eliminate Them](https://jozu.com/blog/top-threats-for-ai-ml-development-and-how-to-eliminate-them/) - AI/ML projects face threats like model drift and security risks. Find out how KitOps can help you overcome these challenges and streamline development.
- [What AI/ML Models Should You Use and Why?](https://jozu.com/blog/what-ai-ml-models-should-you-use-and-why/) - Learn which ML models work best for tasks like classification, clustering, and image recognition tasks. Explore deployment options and key tools in this comprehensive guide.
- [The Fastest Way to Start Your AI Project–Quickstart ModelKits](https://jozu.com/blog/start-your-ai-project-in-minutes-with-jozu-quickstart-modelkits/) - Launch your AI initiatives quickly with Jozu Quickstart ModelKits. Discover how to simplify model selection, data preparation, and project execution.
- [Deploying AI Projects Through a Jenkins Pipeline](https://jozu.com/blog/deploying-ai-projects-through-a-jenkins-pipeline/) - Deploy AI models the smart way with Jenkins pipelines. Step-by-step guide to integrate GitHub, KitOps, and Jozu Hub into your CI/CD workflow.
- [Jozu Hub vs. Docker Hub? Which One Works Best for AI/ML?](https://jozu.com/blog/jozu-hub-vs-docker-hub-which-one-works-best-for-ai-ml/) - Jozu Hub vs. Docker Hub: Learn how Jozu Hub's tailored features for AI/ML projects simplify versioning, GPU setup, and scalability for efficient workflows.
- [How to Use KitOps with MLflow](https://jozu.com/blog/how-to-use-kitops-with-mlflow/) - Learn to use KitOps alongside MLflow to simplify AI project lifecycles. This tutorial covers setup, experiment tracking, and model deployment for scalable ML systems.
- [How to Turn Your OpenShift Pipelines Into an MLOps Pipeline](https://jozu.com/blog/how-to-turn-your-openshift-pipelines-into-an-mlops-pipeline/) - Build smarter MLOps pipelines. Combine KitOps and OpenShift Pipelines to automate deployment, reduce friction, and accelerate machine learning workflows.
- [Platform Engineering vs. MLOps: Key Comparisons](https://jozu.com/blog/platform-engineering-vs-mlops-key-comparisons/) - Discover how Platform Engineering and MLOps compare and complement each other, tackling challenges like infrastructure complexity and model drift while streamlining AI workflows and deployments.
- [Understanding the MLOps Lifecycle](https://jozu.com/blog/understanding-the-mlops-lifecycle/) - Master the MLOps lifecycle with this guide. Understand key stages like CI/CD, monitoring, and retraining, and discover how KitOps tackles common challenges.
- [Why We Need Purpose-Built Platform Engineering Tools for AI/ML](https://jozu.com/blog/why-we-need-purpose-built-platform-engineering-tools-for-ai-ml/) - Discover why AI/ML demands purpose-built platform engineering tools and how Jozu simplifies scalability, collaboration, and innovation.
- [KitOps v1.0.0 is Now Generally Available, Featuring Hugging Face to ModelKit Import](https://jozu.com/blog/kitops-v1-0-0-release-announcement/) - KitOps v1.0.0 is now generally available. This release includes performance improvements and the ability to import models Hugging Face models to ModelKits directly.
- [Accelerating ML Development with DevPods and ModelKits](https://jozu.com/blog/accelerating-ml-development-with-devpods-and-modelkits/) - Learn how DevPods and ModelKits improve ML development by offering pre-configured environments, version-controlled artifacts, and seamless sharing.
- [Deploying ML projects with Argo CD](https://jozu.com/blog/deploying-ml-projects-with-argo-cd/) - Are ML projects hard to scale? Learn how Argo CD and KitOps transform deployment and collaboration for ML workflows.
- [10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows](https://jozu.com/blog/10-must-know-open-source-platform-engineering-tools-for-ai-ml-workflows/) - Don't let complex workflows choke your AI/ML projects. These 10 open source Platform Engineering tools are your ticket to faster, more innovative development.
- [What’s Next for the KitOps Project](https://jozu.com/blog/whats-next-for-the-kitops-project/) - KitOps has been proven production and by some of the most demanding organizations and it’s ready for whatever you can throw at it!
- [We’re submitting KitOps to the CNCF](https://jozu.com/blog/were-submitting-kitops-to-the-cncf/) - To accelerate our industry’s path to an open standard, we have submitted KitOps to the CNCF so that others can more easily contribute to it, and benefit from it.
- [AI Security: How to Protect Your Projects with Hardened ModelKits](https://jozu.com/blog/ai-security-how-to-protect-your-projects-with-hardened-modelkits/) - Protect your AI projects with Jozu Hardened ModelKits. Learn to counter adversarial attacks, data breaches, and model theft for a secure, reliable AI deployment.
- [Unifying Documentation and Provenance for AI and ML: A Developer's Guide to Navigating the Chaos](https://jozu.com/blog/unifying-documentation-and-provenance-for-ai-and-ml-a-developers-guide-to-navigating-the-chaos/) - How to navigate model security, provenance and attestation through Model Cards and AI SBOMs.
- [KitOps: A Practical Approach to Accelerating AI/ML Development to Production](https://jozu.com/blog/kitops-a-practical-approach-to-accelerating-ai-ml-development-to-production/) - In this post, we breakdown how to identify which parts of your existing infrastructure, tools, and processes can be adapted to work with AI/ML and which parts genuinely require a new approach.
- [Empowering Developers with Advanced Machine Learning Knowledge](https://jozu.com/blog/empowering-developers-with-advanced-machine-learning-knowledge/) - Advance your career with Jozu Learning Center, a free resource for software developers who want to learn about ML.
- [KitOps Release v0.2–Introducing Dev Mode and the ability to chain ModelKits](https://jozu.com/blog/kitops-release-v0-2-introducing-dev-mode-and-the-ability-to-chain-modelkits/) - Welcome KitOps v0.2! This update brings two major features for working with LLMs, as well numerous smaller enhancements.
- [Strategies for Tagging ModelKits](https://jozu.com/blog/strategies-for-tagging-modelkits/) - ModelKits, much like other OCI artifacts, can be identified using tags that are comprehensible to humans. This blog explores various strategies for effectively tagging your ModelKits.

## Pages

- [Frontpage](https://jozu.com/) - Jozu is the on-prem Kubernetes AI platform for secure model packaging, registry, and deployment. 7x faster with tamper-proof security.
- [MCP Registry](https://jozu.com/mcp-registry/)
- [Agent Guard](https://jozu.com/agent-guard/)
- [Agent Guardrails Case Study](https://jozu.com/agent-guardrails-case-study/)
- [Agent Guard vs Alternatives](https://jozu.com/agent-guard-vs-alternatives/)
- [Defense](https://jozu.com/defense/)
- [AI Gateway](https://jozu.com/ai-gateway/)
- [Jozu Privacy Policy](https://jozu.com/privacy-policy/) - Last Updated: November 25, 2025 1. Introduction Your privacy is important to us. Jozu is a product of Akara Technologies Inc., a Delaware corporation doing business as Jozu (the "Company"). It is our policy to respect your privacy and comply with applicable laws and regulations regarding the collection, use, and protection of personal information. This
- [jozu-vs-sagemaker](https://jozu.com/jozu-vs-sagemaker/)
- [jozu-vs-mlflow](https://jozu.com/jozu-vs-mlflow/)
- [jozu-vs-docker-hub](https://jozu.com/jozu-vs-docker-hub/)
- [jozu-vs-weights-and-biases](https://jozu.com/jozu-vs-weights-and-biases/)
- [Fast and Secure](https://jozu.com/fast-and-secure/) - Deploy AI models 10x faster with enterprise-grade security. Jozu Hub on-premises: curate, scan, audit, and deploy ML models with confidence.
- [Kubernetes](https://jozu.com/kubernetes/) - The security and governance layer for Kubernetes ML. Jozu hardens KubeFlow pipelines and KServe deployments with scanning, storage, and policy control.
- [Blog](https://jozu.com/blog/) - Insights on MLOps, KitOps, model security, and enterprise AI deployment from the Jozu team.
- [Company](https://jozu.com/company/) - Meet the Jozu team — building the future of secure, scalable AI operations. Backed by Mozilla Ventures, HalfCourt Capital, and more.
- [Security](https://jozu.com/security/) - AI security and governance for self-hosted Kubernetes environments. Tamper-proof model packaging, audit trails, and EU AI Act compliance.
- [Pricing](https://jozu.com/pricing/) - Jozu Hub pricing — from free sandbox to enterprise on-prem deployment. Start securing your ML models today.
- [Product](https://jozu.com/product/) - Jozu Hub: private model registry with security scanning, governance, and inference microservices. Take control of your AI model supply chain.
- [Proof Of Concept Terms And Conditions](https://jozu.com/poc-agreement/) - Last Updated: November 25, 2025 These Proof of Concept Terms and Conditions ("POC Terms") govern the evaluation of Jozu software products and services ("Software") provided by Akara Technologies Inc., a Delaware corporation doing business as Jozu ("Jozu"). The entity on whose behalf an individual requests, receives, or uses Jozu-provided credentials to access the Software is
- [Terms of Service](https://jozu.com/terms-of-service/) - Effective Date: December 1, 2025 1. Introduction These Terms of Service ("Terms") govern your use of the products and services provided by Akara Technologies Inc., a Delaware corporation doing business as Jozu (the "Company"). These Terms apply to: Websites: https://jozu.com and https://jozu.ml Software as a Service (SaaS) applications hosted by Jozu Any related online services
- [On Demand Demo](https://jozu.com/on-demand-demo/)
- [Solutions](https://jozu.com/solutions/)
- [KitOps ModelPack Support](https://jozu.com/kitops-modelpack-support/) - To support enterprise use cases, Jozu offers professional support, guaranteed SLAs, and expert guidance to ensure success at scale. Learn more about enterprise support for open source KitOps and ModelPack
- [Champions](https://jozu.com/champions/)
- [Press and Coverage](https://jozu.com/news/)
- [Case Study](https://jozu.com/case-study/)
- [Join the Jozu mailing list](https://jozu.com/join/)
- [Hardened ModelKits](https://jozu.com/hardened-modelkits/)
- [Docs](https://jozu.com/docs/)
- [Hub](https://jozu.com/hub/)
- [Early Access](https://jozu.com/early-access/)
- [Contact](https://jozu.com/contact/)
- [Acceptable Use](https://jozu.com/acceptable-use/) - Jozu Acceptable Use Policy This acceptable use policy covers Jozu's products, services, and technologies (collectively referred to as the "Products") provided by Akara Technologies, Inc under any ongoing agreement. It’s designed to protect us, our customers and the general Internet community from unethical, irresponsible and illegal activity. Akara Technologies, Inc customers found engaging in activities

## Champions

- [Manav Sutar](https://jozu.com/blog/champion/manave-sutar/)
- [Amitesh Anand](https://jozu.com/blog/champion/amitesh-anand/)
- [Neel Shah](https://jozu.com/blog/champion/neel-shah/)
- [Shivay Lamba](https://jozu.com/blog/champion/shivay-lamba/)
- [Ram Iyengar](https://jozu.com/blog/champion/ram-iyengar/)
- [Burhan Qaddoumi](https://jozu.com/blog/champion/burhan-qaddoumi/)
- [Blaze Kotsenburg](https://jozu.com/blog/champion/blaze-kotsenburg/)
- [Prasanth Bupd](https://jozu.com/blog/champion/prasanth-bupd/)

## Events

- [Co-located Events Europe 2026, Baby-sitting Your AI Models in Production? Stop!](https://jozu.com/blog/event/co-located-events-europe-2026-baby-sitting-your-ai-models-in-production-stop/) - Ram Iyengar challenges the assumption that AI models need constant manual oversight in production, presenting practical strategies for building self-sustaining ML deployment pipelines. Learn how to move beyond reactive model management toward automated monitoring, retraining, and governance workflows that free teams to focus on innovation.
- [DevConf.IN 2026, Bridging DevOps and MLOps: Unifying Pipelines with KitOps and GitOps](https://jozu.com/blog/event/devconf-in-2026-bridging-devops-and-mlops-unifying-pipelines-with-kitops-and-gitops/) - Join Neel Shah at DevConf.IN 2026 to explore how KitOps and GitOps unify DevOps and MLOps pipelines. Learn why pipelines break when AI enters the picture and discover practical patterns for bringing repeatability and governance to AI workflows at scale.
- [DevOpsCon Amsterdam Platform Engineering Summit, Unsigned, Unverified, In Production: The State of ML Supply Chains](https://jozu.com/blog/event/devopscon-amsterdam-platform-engineering-summit-unsigned-unverified-in-production-the-state-of-ml-supply-chains/) - Gorkem Ercan explores the critical gap in ML supply chain security, showing how production ML systems deploy unverified models from Hugging Face with zero attestation. Learn practical approaches to implementing signing, verification, and provenance tracking for ML artifacts using existing tools like OCI registries, Sigstore, and admission controllers.
- [OCX Conference, Unsigned, Unverified, In Production: The State of ML Supply Chains](https://jozu.com/blog/event/ocx-conference-unsigned-unverified-in-production-the-state-of-ml-supply-chains/) - Gorkem Ercan explores the critical gap in ML supply chain security, showing how production ML systems deploy unverified models from Hugging Face with zero attestation. Learn practical approaches to implementing signing, verification, and provenance tracking for ML artifacts using existing tools like OCI registries, Sigstore, and admission controllers.
- [Cloud Native Kochi February Online Meetup, Managing AI/ML Model Provenance and Compliance with KitOps](https://jozu.com/blog/event/cloud-native-kochi-february-online-meetup-managing-ai-ml-model-provenance-and-compliance-with-kitops/) - Join Cloud Native Kochi for a virtual session on managing AI/ML model provenance and compliance using KitOps. Learn how to standardize your ML workflows with OCI-compliant ModelKits and establish proper governance for your AI/ML operations.
- [CNCF Gandhinagar December Meetup, Bridging DevOps and MLOps: Unifying Pipelines with KitOps and GitOps](https://jozu.com/blog/event/cncf-gandhinagar-december-meetup-bridging-devops-and-mlops-unifying-pipelines-with-kitops-and-gitops/) - Join KitOps Champion Neel Shah as he explores how to unify DevOps and MLOps workflows through containerized model packaging and GitOps practices. Learn how KitOps enables teams to bridge the gap between traditional software delivery and ML model deployment.
- [DevFest Pune 2025, Building Secure & Scalable Applications with Generative AI](https://jozu.com/blog/event/devfest-pune-2025-building-secure-scalable-applications-with-generative-ai/) - Discover how Generative AI accelerates development workflows while maintaining security and scalability. This hands-on session covers tools like Antigravity AI Studio, Firebase, and Gen AI APIs for turning prompts into production-ready code.
- [KubeCon + CloudNativeCon NA 2025, The Hidden Risks in AI/ML Supply Chains: How to Secure Your Workloads](https://jozu.com/blog/event/kubecon-cloudnativecon-na-2025-the-hidden-risks-in-ai-ml-supply-chains-how-to-secure-your-workloads/) - Discover the critical security vulnerabilities lurking in AI/ML supply chains and learn practical strategies to protect your workloads from emerging threats. This session will explore real-world attack vectors and demonstrate how to implement robust security measures using cloud-native tools and best practices.
- [Co-located Events NA 2025, Building Secure MLOps Pipelines with KitOps + Argo Workflows](https://jozu.com/blog/event/co-located-events-na-2025-building-secure-mlops-pipelines-with-kitops-argo-workflows/) - Learn how to build production-ready MLOps pipelines by combining KitOps for model packaging with Argo Workflows for orchestration, creating secure and scalable AI/ML deployments. This hands-on session will demonstrate how to automate model deployment workflows while maintaining security and compliance throughout the pipeline.
- [Women in CNCF Presents: cTENcf Celebration Meetup, ModelPack: The Missing Standard for AI Model Packaging](https://jozu.com/blog/event/women-in-cncf-presents-ctencf-celebration-meetup-modelpack-the-missing-standard-for-ai-model-packaging/) - Learn how ModelPack, a CNCF Sandbox project, is redefining MLOps by creating a standard for AI model packaging. This talk bridges DevOps principles with machine learning operations using tools like KitOps to solve the challenges of ML artifact management.
- [KCD Sri Lanka 2025, Standardising AI workflows on Kubernetes for Research](https://jozu.com/blog/event/kcd-sri-lanka-2025-standardising-ai-workflows-on-kubernetes-for-research/) - This technical session explores how to standardize, secure, and scale AI workflows on Kubernetes using KitOps for model-lifecycle packaging and efficient multi-GPU training techniques. Learn practical approaches to managing proprietary AI datasets and models with proper data sovereignty while maximizing GPU utilization for research institutes with limited hardware.
- [Co-located Events NA 2025, Building Secure MLOps Pipelines with KitOps + Argo Workflows](https://jozu.com/blog/event/co-located-events-na-2025/) - Learn how to build secure MLOps pipelines by combining KitOps with Argo Workflows to create robust, production-ready AI deployment systems. The presentation will demonstrate practical integration patterns for securing AI deployments and show how these tools work together to streamline MLOps workflows.
- [KubeCon + CloudNativeCon NA 2025, The Hidden Risks in AI/ML Supply Chains: How to Secure Your Workloads](https://jozu.com/blog/event/kubecon-cloudnativecon-na-2025-november-10-13/) - Explore critical security considerations for AI supply chains and learn how to secure your workloads against emerging threats in machine learning deployments. The presentation will cover practical strategies for identifying and mitigating security risks in AI/ML pipelines and demonstrate secure deployment practices for production AI systems.
- [Mozilla Festival 2025, Democratizing AI/ML Cross-Team Development with GitOps](https://jozu.com/blog/event/mozilla-festival-2025-democratizing-ai-ml-cross-team-development-with-gitops/) - This session explores how KitOps and GitOps methodologies can break down barriers in AI/ML development, enabling seamless collaboration across data science, engineering, and operations teams. Learn practical strategies for standardizing ML workflows, versioning models alongside code, and creating reproducible AI pipelines.
- [Toronto Enterprise DevOps User Group, From Chaos to Control in Enterprise AI/ML](https://jozu.com/blog/event/toronto-enterprise-devops-user-group-october-9/) - Learn how Jozu uses OCI Artifacts to package 100GB+ models, datasets, and code into secure, versioned bundles you can deploy 7x faster than other methods. The presentation will demonstrate taking an MLFlow experiment to a production Kubernetes cluster with just a few lines of YAML.
- [OpenSSF Community Day Korea 2025, Standardizing the Unstandardized: Securing AI Supply Chain With Model-Spec and KitOps](https://jozu.com/blog/event/openssf-community-day-korea-2025-november-4/) - Learn how Model-Spec and KitOps can help standardize and secure the AI supply chain in this technical session focused on securing open source AI/ML workflows. The presentation will explore practical approaches to addressing the unique security challenges in AI/ML pipelines and supply chains.
- [Cloud Native Rejekts Atlanta 2025, Ease The Move From DevOps to MLOps: A Case For ModelSpec + KitOps](https://jozu.com/blog/event/cloud-native-rejekts-atlanta-2025-november-8/) - Learn how to apply cloud-native paradigms to AI workloads using the CNCF Sandbox project ModelSpec and KitOps to bridge the gap between data science and operations teams. The presentation will demonstrate how to create a "Docker"-like interface for AI workloads and efficiently manage models on Kubernetes and other container runtimes.
- [Civo Navigate India 2025, Containerized AI Workflows Transforming Enterprise Deployments](https://jozu.com/blog/event/civo-navigate-india-2025-november-18/) - "Containerized AI Workflows Transforming Enterprise Deployments" Event Details: Date: November 18, 2025 Time: Full day event Location: Four Seasons Hotel, Bengaluru at Embassy ONE, 8, Bellary Road, Ganganagar, Bengaluru, Karnataka, India Event Title: Civo Navigate India 2025: Sovereignty and AI Edition Focus: AI, Data Sovereignty, and Regulatory Impact Expected Attendance: Over 800 cloud and AI

## Categories

- [Uncategorized](https://jozu.com/blog/category/uncategorized/)
- [KitOps](https://jozu.com/blog/category/kitops/)
- [ai](https://jozu.com/blog/category/ai/)
- [devops](https://jozu.com/blog/category/devops/)
- [machinelearning](https://jozu.com/blog/category/machinelearning/)
- [opensource](https://jozu.com/blog/category/opensource/)
- [MLOps](https://jozu.com/blog/category/mlops/)
- [Security](https://jozu.com/blog/category/security/)
- [release notes](https://jozu.com/blog/category/release-notes/)

## Tags

- [News](https://jozu.com/blog/tag/news/) - News articles and updates
- [KubeCon](https://jozu.com/blog/tag/kubecon/) - KubeCon conferences and events
- [Kubernetes](https://jozu.com/blog/tag/kubernetes/) - Kubernetes related content
- [KitOps](https://jozu.com/blog/tag/kitops/) - KitOps platform and tools
- [Events](https://jozu.com/blog/tag/events/) - Conference and event related content
- [On-Premises](https://jozu.com/blog/tag/on-premises/) - On-premises deployment and infrastructure
- [Flux CD](https://jozu.com/blog/tag/flux-cd/) - Flux CD GitOps continuous delivery
- [GitOps](https://jozu.com/blog/tag/gitops/) - GitOps methodology and practices
- [MLOps](https://jozu.com/blog/tag/mlops/) - Machine Learning Operations
- [Python](https://jozu.com/blog/tag/python/) - Python programming and development
- [Marimo](https://jozu.com/blog/tag/marimo/) - Marimo reactive Python notebooks
- [CNCF](https://jozu.com/blog/tag/cncf/) - Cloud Native Computing Foundation
- [ModelPack](https://jozu.com/blog/tag/modelpack/) - ModelPack OCI specification for AI/ML packaging
- [Enterprise AI](https://jozu.com/blog/tag/enterprise-ai/) - Enterprise artificial intelligence and machine learning