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
Influence Sensitivity Plots Explained
This article was co-authored by Jisoo Lee. The world is producing information at an exponential rate, but that may come at the cost of more noise or become too costly. With all this data, it can be increasingly challenging for models to be useful, even as effective...
Building an ML Platform from Scratch – Alon Gubkin, Aporia
This talk was part of the MDLI ops 2022 conference. You can find all the slides here: https://machinelearning.co.il/11415/mdli-ops-2022-slides/ This post has been republished by AIIA. To view the original video, please click HERE.
Version control for data science and machine learning
This article looks at version control for data science and machine learning and has been written following an interview with our DevRel Lead and ex-data scientist Cheuk Ting Ho. During a TerminusDB discovery session, Cheuk mentioned versioned machine learning and it...
Practical Data Centric AI in the Real World
Data-centric AI marks a dramatic shift from how we’ve done AI over the last decade. Instead of solving challenges with better algorithms, we focus on systematically engineering our data to get better and better predictions. But how does that work in the real world?...
Data Monitoring — Be the Master of Your Pipeline
Data monitoring is essential Once your data pipeline reaches a certain complexity, the requirement for some kind of monitoring is unavoidable. When you get the call (hopefully monitoring can help you avoid the call) that a dashboard is broken because data isn’t being...
Simplifying Deployment of ML in Federated Cloud and Edge Environments
Two main challenges are hindering the adoption of AI for enterprises and government agencies. The first is an increase in the need for hybrid solutions to manage data and data science applications, to address data locality in accordance with a rise in regulation and...
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