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
Why do AI attacks exist?
AI algorithms have fundamental flaws that attackers might exploit to cause the system to fail. Unlike typical cybersecurity assaults, these flaws are not the result of programming or human error. They are just flaws in today's cutting-edge methods. To put it another...
What is a Feature Store for Realtime Machine Learning?
Realtime machine learning relies on models, and models are only as good as the data they are built upon. One of the biggest challenges in machine learning is keeping data up-to-date and accurate, and transforming it in a way that the model can ingest it. In most...
A Practical Guide for Debugging Overfitting in Machine Learning
Overview Generating business value is key for data scientists, but doing so often requires crossing a treacherous chasm with 90% of models never reaching production (and likely even fewer providing real value to the business). The problem of overfitting is a critical...
Automated ML Model Deployment: MLFlow and AWS Sagemaker
We all know how difficult it can be to move models from a training environment into production. But what if you could automate your model deployment? Join us for an overview and demo of the benefits of an automated model deployment pipeline that leverages Modzy...
What Is AUC?
In the latest edition of “The Slice,” a blog series from Arize that explains the essence of ML concepts in a digestible question-and-answer format, we dive into how to calculate AUC – and where it’s most useful. Want to see AUC in the Arize platform? Sign up for a...
What to expect from synthetic data. Using YData and Great Expectations
This blog has been republished by AIIA. To view the original article, please click HERE.
Connect with Us
Follow US