AI Infrastructure Alliance

Building the canonical stack for AI/ML

Learn More


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 establishing clean APIs, integration points, and open standards for how different components of a complete enterprise machine learning stack can and should interoperate.  That lets organizations make better decisions about the tools they’ll deploy in the AI/ML application stacks of today and tomorrow.


Version 20200827-002

Read the full mission statement right here.

The AI Infrastructure Alliance’s mission is to help organizations:

1) Establish a canonical stack for Artificial Intelligence (AI) and Machine Learning (ML) Operations (MLOps)

2) Develop ideal best practices and architectures for doing AI/ML at scale in enterprise organizations

3) Foster openness for algorithms, tooling, libraries, frameworks, models and datasets in AI/ML

4) Advocate for technologies, such as differential privacy and homomorphic encryption, that helps anonymize data sets and protect privacy

5) Work towards universal standards to share data between AI/ML applications


(full list on the partners page)


Subscribe to our newsletter to get updates on the AI Alliance, news from core members, and major industry articles.


Open Source Myths and Half-Truths: Part 1

Open Source Myths and Half-Truths: Part 1

In this article from Seldon evangelist, Ryan Dawson, we get a fantastic history of open source and how it's changed in the years since the early Linux revolution. Over time, we've seen a dramatic prolifertion of the new open source models and licenses.  In the early...

Will AutoML Replace Data Scientists?

Will AutoML Replace Data Scientists?

In this article, KD Nuggets authors Joseph Chin, Aifaz Gowani, Gabriel James, and Matthew Peng ask if AutoML services from Amazon, Google and Microsoft will replace data scientists in the long run. The data science pipeline is a complicated one with a lot of manual...

When to Use CPU, GPUs or TPUs

When to Use CPU, GPUs or TPUs

It's an amazing time for data scientists everywhere, as the hardware needed to crunch numbers like never before comes online. It wasn't long ago that there were only a few chip manufacturers in the world, Intel and AMD, working on essentially the same architecture for...

Roads, Where We’re Going We Don’t Need Roads

Roads, Where We’re Going We Don’t Need Roads

There’s an exciting side to AI.  Intense, multi-million dollar research over many years that leads to a billion dollar algorithmic breakthrough that keeps self-driving cars from crashing or detects lung cancer better than ever is glamorous part of intelligent...