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
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ARTICLES
How to Monitor Machine Learning Models in Production like a Pro
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Seldon and Pachyderm – Two Foundational Pieces of the Machine Learning Loop Come Together to Take Your Model from Training to Production
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Why Governance Should Drive Your 2021 ML Strategy
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Maximizing ML Infrastructure Tools for Production Workloads – Arize AI
Businesses in almost every industry are rapidly adopting Machine Learning (ML) technology and understanding the various platforms and offerings can be a challenge. The ML Infrastructure space is crowded, confusing, and complex.
Powering Up Your ML Monitoring in Production Now
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