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
A Data Scientist’s Guide to Identify and Resolve Data Quality Issues: Doing this early for your next project will save you weeks of effort and stress
If you've worked in the AI industry with real-world data, you’d understand the pain. No matter how streamlined the data collection process is, the data we’re about to model is always messy. According to IBM, the 80/20 Rule holds for data science as well. 80% of a data...
Scaling Breast Cancer Detection with Pachyderm
Introduction Breast cancer is a horrible disease that affects millions worldwide. In the US and other high-income countries, advances in medicine and increased awareness have significantly improved the survival rate of breast cancer to 80% or higher. However, in many...
Concept drift in machine learning 101
As machine learning models become more and more popular solutions for automation and prediction tasks, many tech companies and data scientists have adopted the following working paradigm: the data scientist is tasked with a specific problem to solve, they receive a...
Build an AI App in 5 Minutes
This post has been republished by AIIA. To view the original video, please click here: https://www.youtube.com/watch?v=ieOsenjeV8Q
The Who, What, Where, When, Why (and How) of Recommender Systems
An overview of recommendation systems, including how teams should monitor and troubleshoot models in production Learn more about how Arize helps clients observe recommendation systems, sign up for a free account, dive into an interactive demo or request a trial of...
Search relevance evaluation for e-commerce using Toloka
One of the most popular e-commerce tasks today is search relevance evaluation. Online marketplaces rely on search algorithms to improve the customer experience, but evaluating search data is challenging. Dmitrii will talk about pitfalls to avoid and share insider tips...
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