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
Why We Started the AIIA and What It Means for the Rapid Evolution of the Canonical Stack of Machine Learning
A detailed discussion of why we started the AIIA, what our work means for you and what it all means for the rapid evolution of a standardized platform for machine learning.
The New Landscape of Machine Learning for the New Year
Before the holiday seasons, we reached out to AIIA members and researched many of the companies outside the AIIA to build a comprehensive understanding of their capabilities. You may recall that in the early days of AIIA we built a similar graphic but with...
How to Monitor Machine Learning Models in Production like a Pro
More and more machine learning models are deployed to production every day. These models play a crucial role in deciding whether an individual will be authorized a loan they applied for, whether an intrusion detection system will detect suspicious network activity and...
Strategies that Deliver a Big Boost to Your Machine Learning Computational Efficiency
How companies can decrease the cost and increase efficiency of specialized hardware for their machine learning efforts.
Seldon and Pachyderm – Two Foundational Pieces of the Machine Learning Loop Come Together to Take Your Model from Training to Production
In this tutorial we walk through two key pieces of the Machine Learning loop, data versioning and model serving, using Pachyderm and Seldon.
Why Governance Should Drive Your 2021 ML Strategy
Governance is one of the number one challenge organizations face with ML model deployment as they head into 2021.
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