🤗 ml-intern: an open-source ML engineer that reads papers, trains models, and ships ML models
Introducing beginners to the mechanics of machine learning – Miriam Posner
Every year, I spend some time introducing students to the mechanics of machine learning with neural nets. I definitely don’t go into great depth; I usually only have one class for this. But I try to unpack at least some of the major concepts, so that ML isn’t quite such a black box.
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.
Introducing LM Studio Bionic: the AI agent for open models
The AI agent made for open models, built to get things done.
Brittany's Blog | Brittany Ellich | Offprint
Software engineer, newsletter writer, and podcast host writing about AI, accessibility, and building a sustainable engineering career without burning out.
Train AI models with Unsloth and Hugging Face Jobs for FREE
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
OpenThoughts: Data Recipes for Reasoning Models — Ryan Marten, Bespoke Labs
LukeW | Rethinking Networking for the AI/ML Era
In her AI Speaker Series presentation at Sutter Hill Ventures, Google Distinguished Engineer Nandita Dukkipati explained how AI/ML workloads have completely bro...
mlx-examples/stable_diffusion at main · ml-explore/mlx-examples
Examples in the MLX framework. Contribute to ml-explore/mlx-examples development by creating an account on GitHub.
The Applied Machine Learning Collective of the Rockies
We're building a hands-on, community-led space where AI/ML practitioners actually grow together. A modern guild where newcomers learn from journeymen, journeymen sharpen their skills alongside experts, and everyone works on real problems that matter.
Introducing Unsloth Studio | Unsloth Documentation
Run and train AI models locally with Unsloth Studio.
How to Do AI-Assisted Engineering
15 experienced engineers and engineering leaders share their real-world experiences with AI-assisted engineering.
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.
Charting and Navigating Hugging Face's Model Atlas
Charting and Navigating Hugging Face's Model Atlas: an interactive visualization and analysis tool for exploring large-scale AI model repositories. The atlas maps model relationships, and helps identify trends and fill in undocumented regions using structural patterns in the data.
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.
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
