AI INFRASTRUCTURE ALLIANCE

Building the Canonical Stack for Machine Learning

Our Work

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 showing how different components of a complete enterprise machine learning stack can and should interoperate.  We deliver essential reports and research, virtual events packed with fantastic speakers and visual graphics that make sense of an ever-changing landscape.

Download the Enterprise Generative AI Adoption Report

Oct 2023

Our biggest report of the year covers the wide world of agents, large language models and smart apps. This massive guide dives deep into the next-gen emerging  stack of AI, prompt engineering, open source and closed source generative models, common app design patterns, legal challenges, LLM logic and reasoning and more.

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AI Landscape

Check out our constantly updated AI Landscape Graphic that shows the full range of capabilities for major MLOps tools instead of just pigeonholing them into a single box that highlights only one aspect of their primary characteristics.

Today’s MLOps tooling offers a broad sweep of possibilities for data engineering and data science teams.  You can’t easily see those capabilities in typical graphics that show a bunch of logos so we’ve engineered a better info-graphic to let you quickly figure out if a tool does what you need now.

Events – Past and Future

Check here for our upcoming events and to watch videos from past events.  We put on 3 to 4 major events every year and they’re packed with fantastic speakers from across the AI/ML ecosystem.

MEMBERS

ARTICLES

Machine Learning Isn’t Models, It’s Features

Machine Learning Isn’t Models, It’s Features

Why feature engineering and feature management is as important to your ML project as the algorithm you choose There are endless articles and tutorials on topics in Data Science and ML, from Scikit-learn to Keras to PyTorch. In just a few hours, you can create a GAN to...

What’s the difference: JSON diff and patch

What’s the difference: JSON diff and patch

What will the distributed data environment in Web3 look like? How will we have a distributed network of data stores which allow updates and synchronizations? What is it that allows git to perform distributed operations on text so effectively? Is it possible to do the...

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