Building the Canonical Stack for Machine Learning

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.



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



TerminusDB 10.1 – The Mule Release

TerminusDB 10.1 – The Mule Release

The latest release of TerminusDB is here and there’s a lot to tell you about. The Mule Release, a homage to Asimov’s Foundation series, is faster, more robust, and includes new features to make developing knowledge graphs and data-intensive applications easier and...

Understanding ML Monitoring Debt

Understanding ML Monitoring Debt

We’re all familiar with technical debt in software engineering, and at this point, hidden technical debt in ML systems is practically dogma. But what is ML monitoring debt? ML monitoring debt is when model monitoring is overwhelmed by the scale of the ML systems that...

Connect with Us

Follow US