AI Infrastructure Alliance

Building the canonical stack for AI/ML

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WHAT IS THE AI INFRASTRUCTURE ALLIANCE?

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.

SHORT MISSION STATEMENT

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

CORE MEMBERS

(full list on the partners page)

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ARTICLES

What is Data Lineage?

What is Data Lineage?

What is data lineage? At its most basic it’s the history of your data. It tells us where that data comes from, where it lives and how it’s transformed over time. As AI teams pour over data looking for patterns, or process it and massage it to get it into shape for...

Fire Up Data Logging Across Your ML Systems with WhyLabs

Fire Up Data Logging Across Your ML Systems with WhyLabs

Fire up your MLOps with a scalable, lightweight, open source data logging library Co-author: Bernease Herman We are thrilled to announce the open-source package whylogs. It enables data logging for any ML/AI pipeline in a few lines of code. Data logging is a critical...

7 Rules for Bulletproof, Reproducible Machine Learning R&D

7 Rules for Bulletproof, Reproducible Machine Learning R&D

So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me.  Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning...