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10 Underground AI/ML Tools That Actually 100x Developer Productivity
10 Underground AI/ML Tools That Actually 100x Developer Productivity The secret stack behind the fastest AI devs. The Real AI/ML Productivity Problem AI/ML developers waste 60%+ of their time on …
The Complete Guide to ML Model Versioning: Why Git Alone Won't Save You
How to stop losing your best models and start shipping ML systems that actually work in production
What's 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
MLFlow + KitOps: The Missing Link in ML Reproducibility
How to package your MLFlow experiments into tamper-proof, deployable artifacts MLFlow solves experiment tracking. KitOps solves artifact packaging. Together, they solve the real problem
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.
The State of OCI Artifacts for AI/ML
I’ve spent the last 18 months watching OCI artifacts for AI/ML go from “interesting idea” to production-grade infrastructure in enterprises. OCI is becoming the standard for Kubernetes-native ML deployments where governance, provenance, supply chain controls, and container-centric operations matter.
Your ML Pipeline Is Broken Because You're Not Versioning Anything That Matters
Most teams debug by archaeology. Here's how to fix it with version-controlled packaging and instant rollbacks.
How to Deploy ML Models Like Code
Or: The day I stopped manually deploying models and started actually sleeping at night
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. -
The Game-Changer Your ML Projects Have Been Waiting For: KitOps and OCI Artifacts
Stop wrestling with ML deployment chaos. Start shipping like the pros.
Lab Notes: Intel AI Inference Platform MVP
With the cluster in place, I built the second layer of the stack.
How to Turn ML Training Notebook into 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.
Top Open Source Projects for Beginners
Explore 25+ beginner-friendly open source projects in 2025. Contribute to freeCodeCamp, Habitica, Zulip & more to enhance your coding skills and build your portfolio.
AI Crush 2025 Talk–Secure ML Packaging Using Open Source KitOps
Join our free global online conference and learn directly from practitioners who are scaling AI solutions today. Whether you're leading strategy or engineeri...
Stop Vibe Coding 🚫 Make Your AI SaaS Production-Ready with these 7 Tools ⚡️
Vibe Coding is Fun, but It's Not Production Ready! If you’re building an AI SaaS that’s actually... Tagged with ai, webdev, opensource, api.
Cloud Native Podcast–KitOps: An Open Standard For Packaging, Managing & Deploying Machine Learning Models
Deploying machine learning models from Jupyter notebooks to production is a complex and fragmented process. Engineers often struggle with inconsistent packag...
Machine Learning Packaging for Ops with KitOps
Manage your AI and ML files, git repos, containers, and artifacts with YAML and the KitOps ModelKit proposed standard. A CNCF Sandbox project to standardize ...
Tutorial–Maintaining ML Model Accuracy With Automated Drift Detection
This tutorial demonstrates how to implement automated drift detection, triggers alerts, and automatically retrains models to maintain accuracy in production environments.
PyData Global Talk–Streamlining AI development and Deployment with KitOps
www.pydata.orgAs organizations increasingly integrate and adopt AI and machine learning internally, the challenge of maintaining separate pipelines for ML-po...
Securing MCP: Applying Lessons Learned from the Language Server Protocol
Given my experience with LSP, I’m enthusiastic about the growing interest in the Model Context Protocol (MCP). However, I am concerned that the valuable lessons learned from LSP are not being effectively applied to MCP.
KubeCon Europe London 2025 – 27 open-source projects you should check out
KubeCon 2025 highlights 27 open-source projects tackling key challenges in Kubernetes, from security to cluster management. Tools like Shipwright, Koordinator, and LoxiLB simplify deployment, networking, and scaling.
Building, Deploying, and Monitoring a Model with KitOps and AWS DevOps Guru
Building, Deploying, and Monitoring a Model with KitOps and AWS DevOps Guru
Open Source Tools for Scaling ML Workloads
Open Source Tools for Scaling ML Workloads
Automating ML Pipeline with ModelKits + GitHub Actions - Jozu MLOps
Automating ML Pipeline with ModelKits + GitHub Actions
How to Build a Secure Hugging Face Deployment Pipeline with Dagger.io, KitOps, and Jozu Hub
How to Build a Secure Hugging Face Deployment Pipeline with Dagger.io, KitOps, and Jozu Hub
A Golden Path to Production — Why Platform Engineering is the Key to Scaling AI/ML Workflows
Scaling AI/ML workflows doesn't have to be complex. Discover how Platform Engineering simplifies the process.
10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows - Jozu MLOps
10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
Deploying ML projects with Argo CD - Jozu MLOps
Deploying ML projects with Argo CD
Accelerating ML Development with DevPods and ModelKits - Jozu MLOps
Accelerating ML Development with DevPods and ModelKits
Secure your model development lifecycle
Secure your model development lifecycle
What’s Next for the KitOps Project - Jozu MLOps
What’s Next for the KitOps Project
KitOps v1.0.0 is Now Generally Available, Featuring Hugging Face to ModelKit Import - Jozu MLOps
KitOps v1.0.0 is Now Generally Available, Featuring Hugging Face to ModelKit Import
DOP 299: Managing Your AI Workloads With KitOps
DevOps Paradox: Managing Your AI Workloads With KitOps
Understanding the MLOps Lifecycle - Jozu MLOps
Understanding the MLOps Lifecycle
Platform Engineering vs. MLOps: Key Comparisons - Jozu MLOps
Platform Engineering vs. MLOps: Key Comparisons
How to Turn Your OpenShift Pipelines Into an MLOps Pipeline - Jozu MLOps
How to Turn Your OpenShift Pipelines Into an MLOps Pipeline
How to Use KitOps with MLflow - Jozu MLOps
How to Use KitOps with MLflow
Building an MLOps pipeline with Dagger.io and KitOps
Lightening Talk–Building an MLOps pipeline with Dagger.io and KitOps
How to Use KitOps with MLflow - Jozu MLOps
How to Use KitOps with MLflow
Jozu Hub vs. Docker Hub? Which One Works Best for AI/ML? - Jozu MLOps
Jozu Hub vs. Docker Hub? Which One Works Best for AI/ML?
Deploying AI Projects Through a Jenkins Pipeline - Jozu MLOps
Deploying AI Projects Through a Jenkins Pipeline
We’re submitting KitOps to the CNCF - Jozu MLOps
We’re submitting KitOps to the CNCF
20 Open Source Tools I Recommend to Build, Share, and Run AI Projects - Jozu MLOps
20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
The Fastest Way to Start Your AI Project–Quickstart ModelKits - Jozu MLOps
The Fastest Way to Start Your AI Project–Quickstart ModelKits
AI Security: How to Protect Your Projects with Hardened ModelKits - Jozu MLOps
AI Security: How to Protect Your Projects with Hardened ModelKits
Top 5 open-source MLOps tool to boost your production ✨
Top 5 open-source MLOps tool to boost your production
Gorkem Ercan - Eclipse, AI/ML, CI/CD
Gorkem Ercan - Eclipse, AI/ML, CI/CD
Revolutionizing MLOps: Gorkem Ercan on Jozu's Game-Changing Solutions for AI Integration
Revolutionizing MLOps: Gorkem Ercan on Jozu's Game-Changing Solutions for AI Integration
TestGuild Devops Toolchain Podcast: Simplifying the AI/ML to Production Pipeline with Görkem Ercan
Simplifying the AI/ML to Production Pipeline with Görkem Ercan
10 MLOps Tools That Comply With the EU AI Act - Jozu MLOps
10 MLOps Tools That Comply With the EU AI Act
Building an MLOps pipeline with Dagger.io and KitOps - Jozu MLOps
Building an MLOps pipeline with Dagger.io and KitOps
Free Online Tutorials to Help You Develop Machine Learning Applications - Jozu MLOps
Free Online Tutorials to Help You Develop Machine Learning Applications
Top 5 Production-Ready Open Source AI Libraries for Engineering Teams - Jozu MLOps
Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
From Proprietary Data to Expert AI with Lamini and KitOps - Jozu MLOps
From Proprietary Data to Expert AI with Lamini and KitOps
Critical LLM Security Risks and Best Practices for Teams - Jozu MLOps
Critical LLM Security Risks and Best Practices for Teams
Access Denied
Enhance LLMs and streamline MLOps using InstructLab and KitOps
Turn DevOps to MLOps Pipelines With This Open-Source Tool - Jozu MLOps
Turn DevOps to MLOps Pipelines With This Open-Source Tool
Turn Your Existing DevOps Pipeline Into an MLOps Pipeline With ModelKits - Jozu MLOps
Turn Your Existing DevOps Pipeline Into an MLOps Pipeline With ModelKits
From Jupyter Notebook to deployed application in 4 steps - Jozu MLOps
From Jupyter Notebook to deployed application in 4 steps.
10 Open Source MLOps Projects You Didn’t Know About - Jozu MLOps
10 Open Source MLOps Projects You Didn’t Know About.
How to Tune and Deploy Your First Small Language Model (SLM) - Jozu MLOps
How to Tune and Deploy Your First Small Language Model (sLLM).
Tools to ease collaboration between data scientists and application developers - Jozu MLOps
Tools to ease collaboration between data scientists and application developers.
25 Open Source AI Tools to Cut Your Development Time in Half - Jozu MLOps
25 Open Source AI Tools to Cut Your Development Time in Half.
A Step-by-Step Guide to Building and Distributing a Sleek RAG Pipeline
In this article, we build a Retrieval-Augmented Generation (RAG) pipeline using KitOps, integrating tools like ChromaDB for embeddings, Llama 3 for language models, and SentenceTransformer for embedding models.
How to turn a Jupyter Notebook into a deployable artifact - Jozu MLOps
From Jupyter Notebook to production-ready artifact: explore our guide to using KitOps and ModelKit for seamless deployment.
How to turn a Jupyter Notebook into a deployable artifact - Jozu MLOps
How to turn a Jupyter Notebook into a deployable artifact.
A step-by-step guide to building an MLOps pipeline - Jozu MLOps
Exploring the steps and processes of building an MLOps pipeline.
How to How to Improve Collaboration Between Enterprises and AI/ML Teams
Let's dive into the dynamic relationship between enterprises and AI/ML teams with Brad Micklea, Founder & CEO of Jozu and project lead for kitops.org. Brad shares valuable insights on bridging the gap and improving the collaboration between these entities. From common challenges to effective strategies, Brad sheds light on the crucial role of communication, alignment, and AI/ML literacy in driving successful collaborations.
I fine-tuned my model on a new programming language. You can do it too! 🚀
I have been using OpenAI ChatGPT-4 for a while now. I don't have a lot of bad stuff to say about... Tagged with webdev, javascript, beginners, programming.
Why enterprise AI projects are moving too slowly
This post lists the challenges with getting an AI/ML project from development into production and offers suggestions on organizational and tooling changes (like KitOps' ModelKits) that can help. Tagged with devops, ai, opensource, aiops.
KitOps: The Bridge Between AI/ML Models and DevOps
Yesterday, Brad Micklea, Jozu CEO and KitOps maintainer, was a guest on the Partially Redacted podcast hosted by Sean Falconer. The 45-minute conversation covered a lot of ground. Specifically, the current state of the KitOps project, where the project is headed, and some of our early ideas for productizing and releasing Jozu, which builds on top of KitOps. In this post I dive a bit deeper into a few of these topics.
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.
Balancing Innovation and Responsibility in AI/ML Deployment with Jozu's Brad Micklea
Listen to this episode from Partially Redacted: Data Privacy, Security & Compliance on Spotify. In this episode, we dive into the world of MLOps, the engine behind secure and reliable AI/ML deployments. MLOps focuses on the lifecycle of machine learning models, ensuring they are developed and deployed efficiently and responsibly. With the explosion of ML applications, the demand for specialized tools has skyrocketed, highlighting the need for improved observability, auditing, and reproducibility. This shift necessitates an evolution in ML toolchains to address gaps in security, governance, and reliability. Jozu is a platform founded to tackle these very challenges by enhancing the collaboration between AI/ML and application development teams. Jozu aims to provide a comprehensive suite of tools focusing on efficiency throughout the model development and deployment process. This conversation discusses the importance of MLOps, the limitations of current tools, and how Jozu is paving the way for the future of secure and reliable ML deployments.
Beyond Git: A New Collaboration Model for AI/ML Development
Git is optimized to work with large numbers of small files, like text files. This alone makes Git impractical for managing such datasets.
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... Tagged with ai, devops, opensource, machinelearning.
Do data scientists really like Git?
I have a theory: data scientists do not like Git.