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 AI Infrastructure Report
With hundreds of AI/ML infrastructure tools on the market, how do you make sense of it all?
Our first annual AI Infrastructure Ecosystem report answers these questions and more. It gives team leads, technical executives and architects the keys they need to build or expand your infrastructure by providing a comprehensive and clear overview of the entire AI/ML infrastructure landscape.
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
The Only 3 ML Tools You Need
Image by Author At a rapid pace, many machine learning techniques have moved from proof of concepts to powering crucial pieces of technology that people rely on daily. In attempts to capture this newly unlocked value, many teams have found themselves caught up in the...
Seven Reasons Why Realtime Machine Learning Is Here To Stay
A very powerful trend is playing out right now — more and more top tech companies are making a larger part of their machine learning as realtime as possible. So much so that many are skipping the offline phase [1] and directly starting with realtime ML systems. More...
The Shapley Value for ML Models: What is a Shapley value, and why is it crucial to many explainability techniques?
This post was co-written with David Kurokawa. In our previous post, we made a case for why explainability is a crucial element to ensuring the quality of your AI/ML model. We also introduced a taxonomy of explanation methods to help compare and contrast different...
Four Steps to Make ML Models Run Faster in Production
Speed and efficiency are the name of the game when it comes to production ML, but it can be difficult to optimize model performance for different environments. In this talk, we dive into techniques you can use to make your ML models run faster on any type of...
The Success of AI Depends on the Speed of Iteration: An MLOps Strategy for AI Models in Manufacturing
We are living in the age of artificial intelligence (AI), a technology that has made itsway into every industry and is advancing at an unprecedented pace. Epitomizing the innovations in AI is the hyperscale AI model. The number ofparameters, which serves as an...
Take My Drift Away
This blog was written in collaboration with Hua Ai, Data Science Manager at Delta Air Lines. In this piece, Hua and Aparna Dhinakaran, CPO and co-founder of Arize AI, discuss how to monitor and troubleshoot model drift. As an ML practitioner, you probably have heard...
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