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
Drift Metrics: How to Select the Right Metric to Analyze Drift
In our last post we summarized the problem of drift in machine learning deployments (“Drift in Machine Learning: Why It’s Hard and What to Do About It” in Towards Data Science). One of the takeaways from the article is: methods for dealing with drift must identify...
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
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...
Moving from Model-Centric to Data-Centric AI
Introduction: AI is a rapidly evolving field and up until a few years ago there were distinct model architectures for different tasks, for example, CNN was the go-to for vision tasks and LSTM like networks for language tasks. Then, with the invention of the...
Detecting Intersectional Unfairness in AI: Part 1
This blog series focuses on unfairness that can be obscured when looking at data or the behavior of an AI model according to a single attribute at a time, such as race or gender. We first describe a real-world example of bias in AI and then discuss fairness,...
9 Reasons Why You Need an Immutable Database
An immutable database means the data within it cannot be deleted or modified. There are numerous reasons why an immutable database is beneficial for you and this article explains some of those arguments. Martin Kleppmann, who is a serial entrepreneur and...
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