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
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...
Building an ML Platform from Scratch – Alon Gubkin, Aporia
This blog has been republished by AIIA. To view the original article, please click HERE.
Version control for data science and machine learning
This article looks at version control for data science and machine learning and has been written following an interview with our DevRel Lead and ex-data scientist Cheuk Ting Ho. During a TerminusDB discovery session, Cheuk mentioned versioned machine learning and it...
Practical Data Centric AI in the Real World
This blog has been republished by AIIA. To view the original article, please click HERE.
Data Monitoring — Be the Master of Your Pipeline
Data monitoring is essential Once your data pipeline reaches a certain complexity, the requirement for some kind of monitoring is unavoidable. When you get the call (hopefully monitoring can help you avoid the call) that a dashboard is broken because data isn’t being...
Simplifying Deployment of ML in Federated Cloud and Edge Environments
Two main challenges are hindering the adoption of AI for enterprises and government agencies. The first is an increase in the need for hybrid solutions to manage data and data science applications, to address data locality in accordance with a rise in regulation and...
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