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.  Also connect with us on social media.


Powering Up Your ML Monitoring in Production Now

Powering Up Your ML Monitoring in Production Now

Today's enterprises rely on machine learning-powered predictions to guide business strategy, such as by forecasting demand and mitigating risk. For an increasing number of businesses, machine learning (ML) underpin their core business model, like financial...