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
AI Infrastructure Alliance Announces DataRobot as a Partner
The AI Infrastructure Alliance (AIIA) announced today that DataRobot joined the Alliance, expanding the rapidly growing list of industry-leading AI companies and communities working together across the world. With its expertise across the entire AI lifecycle, from...
Machine Learning Monitoring: why it matters and how to get it right
Avoid these common ML monitoring mistakes – your model’s success hangs in the balance. So you’ve built a machine learning model that works well in the lab. You’ve validated it, gotten the green light from the internal stakeholders, ensured that it met any regulatory...
Fix your models by fixing your Datasets
By: Atindriyo Sanyal, Vikram Chatterji, Nidhi Vyas, Ben Epstein, Nikita Demir, Anthony Corletti Abstract The quality of underlying training data is very crucial for building performantmachine learning models with wider generalizabilty. However, current machinelearning...
What is MLOps?
Machine learning (ML) becomes effective once models are in production. Organizations, on the other hand, usually underestimate the complexity and challenges of implementing machine learning in production, devoting the majority of their resources to ML development and...
Designing APIs for AI
It’s estimated that anywhere from 50-90% of AI models developed never make it past the AI “valley of death” that exists between the lab and production deployment. This tech talk covers how an API-based approach to building and maintaining AI-enabled applications can...
A Modern Approach to Versioning Large Files for Machine learning and more
Original article (Mandarin) written by ArtiV developer Chen-en Lu (Popcorny) for the MLOps Taiwan Facebook community. Translated and adapted to English by Dave Flynntl;dr ArtiV is smart version control system for large files, especially suited for use on machine...
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