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

Contextual Relevance in Ad Ranking

Contextual Relevance in Ad Ranking

In this talk we’ll look at why contextual relevance in ad ranking and ad targeting is a must-have for a successful ad campaign; what are the current state-of-the-art machine learning solutions in the field of contextual advertising and ad ranking; and how...

Beyond Monitoring: The Rise of Observability

Beyond Monitoring: The Rise of Observability

By: Aparna Dhinakaran & Barr Moses As data and machine learning ecosystems become increasingly complex and companies ingest more and more data, it’s important that data and ML engineering teams go beyond monitoring to  understand the health of their data-driven...

The Key to Sustainable AI is MLOps

The Key to Sustainable AI is MLOps

Practical Green MLOps A manifesto on environmentally sustainable AI infrastructure When we consider the environmental impact of AI and MLOps, our attention is usually focused on the significant energy usage for the compute cycles required for training. After all, a...

Data Reliability Automated with PipeRider

Data Reliability Automated with PipeRider

tl;dr PipeRider is an open-source data reliability toolkit for identifying data quality issues across pipelines. PipeRider was created after months of industry research and it’s available now. Start learning more about the quality of your data by taking PipeRider for...

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