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
Designing a Fairness Workflow for Your ML Models
How do you ensure your model is fair from start to finish? Co-authored by Russell Holz. In the first blog post of this series, we discussed three key points to creating a comprehensive fairness workflow for ensuring fairness for machine learning model outcomes. They...
AI Explainability
This blog has been republished by AIIA. To view the original article, please click HERE.
When Machine Learning Meets Privacy
MLOps.community By Demetrios Brinkmann Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations. Listen on Spotify This blog has been republished by AIIA....
Solving Data Quality with ML Observability and Data Operations
Ensuring high quality for structured data (with ML observability) and unstructured data (with Data Operations) This is a joint piece written in partnership with Superb AI Introduction As the practice of machine learning (ML) becomes more similar to that of other...
AI Deployment Challenges — It’s all about the data
Galaxy Summit 2022 was held this week(03/29), with this year’s topic being ‘The Future Enterprise’. It’s difficult to talk about the future of anything without the subject of AI coming up, so naturally we were happy to get involved. InfuseAI CEO, CL Kao, hosted the...
Building a Real-Time ML Pipeline with a Feature Store
One of the most difficult challenges in operationalizing machine learning is feature engineering with live or production data. Generating features from real-time or online production data is far more complex than with historical data, and requires dedicated...
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