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

MEMBERS

ARTICLES

How do you know you can trust your data?

How do you know you can trust your data?

Every decision in business is made based on supporting data. “Data-driven” is more than just a buzzword for meetings, it’s a way for a company to be self-aware. Using metrics derived from all sorts of data, it’s possible to understand the performance of each...

Automatic ML Model Containerization

Automatic ML Model Containerization

Containerizing machine learning models can be a pain. This talk covers a new open-source approach to building machine learning (ML) models into container images to run in production for inference. Chassis.ml and the Open Model Interface are changing the game with a...

What Are the Prevailing Explainability Methods?

What Are the Prevailing Explainability Methods?

Welcome to “The Slice,” a new blog series from Arize that explains the essence of ML concepts in a digestible question-and-answer format. Learn more about how Arize can help you tackle explainability or request a trial. What Is Explainability in Machine Learning? The...

Influence Sensitivity Plots Explained

Influence Sensitivity Plots Explained

This article was co-authored by Jisoo Lee. The world is producing information at an exponential rate, but that may come at the cost of more noise or become too costly. With all this data, it can be increasingly challenging for models to be useful, even as effective...

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