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
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...
Understanding Types of AI Attacks
Executive Summary AI attacks pose a threat to physical safety, privacy concerns, digital identity safety, and national security, making it crucial for organizations to identify the types of AI attacks and take measures to safeguard their products against them. The...
Challenges of Building Realtime Machine Learning Pipelines
Realtime machine learning is on the rise, and as companies start introducing realtime into their ML pipelines, they are finding themselves having to weigh the trade-offs between performance, cost, and infrastructure complexity, and determine which to prioritize. In...
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