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
Best Practices In ML Observability for Click-Through Rate Models
Total digital ad spending is expected to reach $455.3 billion this year. Of that, 55.2% will go to display advertising and 40.2% will go to search. With digital formats commanding the lion’s share of ad dollars, marketers need to continuously optimize the...
How to Validate the Quality of Your Synthetic Data
In this article, we take you through an end-to-end use-case where you use the original data to train a model that synthetic data, validate the synthetic data quality standards against original data using the Great Expectations library.
AI Infrastructure Alliance Announces NVIDIA as Platinum Sponsor
The AI Infrastructure Alliance (AIIA) today announced that NVIDIA has joined as a platinum sponsor, expanding the growing list of industry-leading AI companies and communities working with the alliance. With its expertise across multiple industries and technologies, NVIDIA plans to work with the AIIA community to accelerate AI deployments and simplify AI/ML for all enterprise and community practitioners.
![An Introduction to Bounding Boxes [+ Best Practices]](https://ai-infrastructure.org/wp-content/uploads/2022/03/Feature-image-400x250.jpeg)
An Introduction to Bounding Boxes [+ Best Practices]
Introduction These days, artificial intelligence is a part of our everyday lives. Whether it’s the sensors in our cars telling us not to switch lanes or a camera filter on our smartphones, machine learning algorithms are being put to work. In the past 20 years, we’ve...
Deep dive into deepfake detection
A combination of “deep learning” and “fake media,” a deepfake is manipulated video content that is nearly indistinguishable from authentic video. Deepfake creators often target celebrities, world leaders, and politicians, manipulating the video to portray the subject...
Why We Started the AIIA and What It Means for the Rapid Evolution of the Canonical Stack of Machine Learning
A detailed discussion of why we started the AIIA, what our work means for you and what it all means for the rapid evolution of a standardized platform for machine learning.
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