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

Fix your models by fixing your Datasets

Fix your models by fixing your Datasets

By: Atindriyo Sanyal, Vikram Chatterji, Nidhi Vyas, Ben Epstein, Nikita Demir, Anthony Corletti Abstract The quality of underlying training data is very crucial for building performantmachine learning models with wider generalizabilty. However, current machinelearning...

What is MLOps?

What is MLOps?

Machine learning (ML) becomes effective once models are in production. Organizations, on the other hand, usually underestimate the complexity and challenges of implementing machine learning in production, devoting the majority of their resources to ML development and...

Designing APIs for AI

Designing APIs for AI

It’s estimated that anywhere from 50-90% of AI models developed never make it past the AI “valley of death” that exists between the lab and production deployment. This tech talk covers how an API-based approach to building and maintaining AI-enabled applications can...

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