AI INFRASTRUCTURE ALLIANCE

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.

About

OUR MISSION

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

ARTICLES

What’s the difference: JSON diff and patch

What’s the difference: JSON diff and patch

What will the distributed data environment in Web3 look like? How will we have a distributed network of data stores which allow updates and synchronizations? What is it that allows git to perform distributed operations on text so effectively? Is it possible to do the...

Understanding Bias & Fairness in Machine Learning

Understanding Bias & Fairness in Machine Learning

Machine learning and big data are becoming ever more prevalent, and their impact on society is constantly growing. Numerous industries are increasingly reliant on machine learning algorithms and AI models to make critical decisions that impact both business and...

Connect with Us

Follow US