Are you looking for a practical, reproducible way to take a machine learning project from raw data all the way to a deployed, production-ready model? This post is your blueprint for the AI/ML lifecycle: you’ll learn how to use Kubeflow and open source tools such as Feast to build a workflow you can run on your laptop and adapt to your own projects.
Designing machine learning systems: an iterative process for production-ready applications
"Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references."--Amazon.com
Model Context Protocol - MLOps Community
The MLOps Community fills the swiftly growing need to share real-world Machine Learning Operations best practices from engineers in the field.
Models.dev — An open-source database of AI models
Models.dev is a comprehensive open-source database of AI model specifications, pricing, and features.
The future of Kubernetes and cloud infrastructure
New development approaches and open-source tools are set to address the complexity and scaling challenges of Kubernetes and evolve cloud infrastructure as we know it today.
Getting Started with ML and AI in Research Software | Software Sustainability Institute
Getting started with ML in research software means embracing a shift in how results are produced and reproduced. Instead of a fixed execution path, research software teams work with systems whose behaviour emerges from data, configuration, and training dynamics. Reproducibility becomes a matter of capturing the process rather than relying solely on the code. The tools and techniques outlined here can be adopted incrementally into existing projects, and together they provide a practical foundation for reproducible ML research.
AI native industrial data platform for manufacturing | UMH
Standardize industrial data across sites and systems to reduce costs, improve efficiency and accelerate execution. Open-source, deployed at production sites across Europe, live in weeks.
Developer Tools for Cloud Development | Red Hat Developer
Development using Kubernetes means all new processes and skills. Find the tools you need to build in the cloud.
Cloudflare Workers AI | Open-source AI inference
Workers AI facilitates the scalable development & deployment of AI applications at the edge.
Im going back to writing code by hand
I vibe-coded a GPU-aware Kubernetes TUI for 7 months, archived it, and started over. Here's what AI gets wrong when projects grow complex.
Running a PDS in Kubernetes for fun & profit - at://hayden.moe
The trials and tribulations of running a PDS on self-hosted Kubernetes with the Gateway API and other such fun things
n8n.io - AI workflow automation tool
n8n is a free and source-available workflow automation tool
Introducing dtoolAI — dtoolAI 0.1.0 documentation
dtoolAI is a Python library to make reproducible AI model training and use easier. The dtoolAI package provides:
Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Generative artificial intelligence (AI) and machine learning (ML) models are being adopted across a variety of domains. As these technologies develop, there is notable diversity in their levels of availability and paths of diffusion. For example, fully closed-source models may be available through chatbots and API calls, but their weights, source code, training data, and other artifacts remain hidden from view. In contrast, open models make some or all of these materials publicly available for developers and downstream users.
GradientHQ/parallax
Parallax is a distributed model serving framework that lets you build your own AI cluster anywhere
Primer: How Kubernetes Came to Be, What It Is, and Why You Should Care
An explantory intruduction to Kubernetes and cloud native computing.
