AI INFRASTRUCTURE ALLIANCE
Building the Canonical Stack for Machine Learning
Our Work
At the AI Infrastructure Alliance, we’re dedicated to bringing together the essential building blocks for the Artificial Intelligence applications of today and tomorrow.
Right now, we’re seeing the evolution of a Canonical Stack (CS) for machine learning. It’s coming together through the efforts of many different people, projects and organizations. No one group can do it alone. That’s why we’ve created the Alliance to act as a focal point that brings together many different groups in one place.
The Alliance and its members bring striking clarity to this quickly developing field by highlighting the strongest platforms and showing how different components of a complete enterprise machine learning stack can and should interoperate. We deliver essential reports and research, virtual events packed with fantastic speakers and visual graphics that make sense of an ever-changing landscape.
Download the Enterprise Generative AI Adoption Report
August 2023
We surveyed over 1000 companies with more than 1B USD in revenue to see how fast they’re adopting generative AI and what their challenges are as they embrace these powerful new systems.
We focused on C-suite and team leaders with job titles like CIO, CTO, Head of AI, VP of Data or VP of Artificial Intelligence, across a range of verticals, everything from law firms, to manufacturing, to telecom, energy, food, healthcare and more.
Get it now. FREE.
AI Landscape
Check out our constantly updated AI Landscape Graphic that shows the full range of capabilities for major MLOps tools instead of just pigeonholing them into a single box that highlights only one aspect of their primary characteristics.
Today’s MLOps tooling offers a broad sweep of possibilities for data engineering and data science teams. You can’t easily see those capabilities in typical graphics that show a bunch of logos so we’ve engineered a better info-graphic to let you quickly figure out if a tool does what you need now.
Events – Past and Future
Check here for our upcoming events and to watch videos from past events. We put on 3 to 4 major events every year and they’re packed with fantastic speakers from across the AI/ML ecosystem.
MEMBERS
ARTICLES
Machine Learning at the Edge: Elements Needed for Scale
Learn about the elements you need to build an efficient, scalable edge ML architecture. There are four components that can help bring order to the chaos that is running ML at the edge and allow you to build an efficient, scalable edge ML architecture: Central...
When Machine Learning meets privacy
Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations. This posted has been republished by AIIA. To listen to the original podcast, please click HERE....
Centaur at Work: Writing a Newsletter
A Story of Many Failures and One Success In this blog, join me as I embark on the complex journey of semi-automating an AI-focused weekly newsletter. You'll get a first-hand look at a diverse set of tools and tactics that I've put to work on this real-life puzzle....
How To Fine-Tune Hugging Face Transformers on a Custom Dataset
Language models have come a long way in recent years, and their capabilities have expanded rapidly. With the right prompt, a language model can generate text that is almost indistinguishable from what a human would produce. In this post, we'll explore the art of...
Reducing GPU Costs for Production AI
This tech talk explores how you can efficiently use GPU resources for production inference. There are several ways to reduce GPU costs for production AI, including using cost-effective GPU options, using cloud providers, using containerization, using GPU acceleration...
The Only 3 ML Tools You Need
Image by Author At a rapid pace, many machine learning techniques have moved from proof of concepts to powering crucial pieces of technology that people rely on daily. In attempts to capture this newly unlocked value, many teams have found themselves caught up in the...
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