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.



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


How the Canonical Stack for Machine Learning will Unleash the Next Generation of Cutting Edge AI Apps

How the Canonical Stack for Machine Learning will Unleash the Next Generation of Cutting Edge AI Apps

The rise of the canonical stack in machine learning will change the way every company builds and develops AI/ML apps in the future, making it easier and faster to get started with advanced data science. Now companies will no longer need to build their own infrastructure from scratch. They can get right to building cutting edge models fast.

The MLOps Stack

The MLOps Stack

What is MLOps (briefly) MLOps is a set of best practices that revolve around making machine learning in production more seamless. The purpose is to bridge the gap between experimentation and production with key principles to make machine learning reproducible,...

What is a Feature Store

What is a Feature Store

Data teams are starting to realize that operational machine learning requires solving data problems that extend far beyond the creation of data pipelines. In Tecton’s previous post, Why We Need DevOps for ML Data, we highlighted some of the key data challenges that...

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