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.
Get it now. FREE.
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
How to go from raw data to production like a pro
An odyssey on improving data quality with synthetic data and model delivery with MLOps Machine Learning and AI are two concepts that definitely have changed our way of thinking in the last decade, and will probably change even more in the next few years. But, we are...
See Pachyderm in Action | Under 5 Minute Demo
This post has been republished by AIIA. To view the original video, please click HERE.
How do you know you can trust your data?
Every decision in business is made based on supporting data. “Data-driven” is more than just a buzzword for meetings, it’s a way for a company to be self-aware. Using metrics derived from all sorts of data, it’s possible to understand the performance of each...
How to Build Real-Time Feature Engineering with a Feature Store
Simplifying feature engineering for building real-time ML pipelines might just be the next holy grail of data science. It’s incredibly difficult and highly complex, but it’s also desperately needed for multiple use cases across dozens of industries. Currently,...
Automatic ML Model Containerization
Containerizing machine learning models can be a pain. This talk covers a new open-source approach to building machine learning (ML) models into container images to run in production for inference. Chassis.ml and the Open Model Interface are changing the game with a...
What Are the Prevailing Explainability Methods?
Welcome to “The Slice,” a new blog series from Arize that explains the essence of ML concepts in a digestible question-and-answer format. Learn more about how Arize can help you tackle explainability or request a trial. What Is Explainability in Machine Learning? The...
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