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
Data Exchange Podcast (Episode 79) Hyun Kim
This week’s guest is Hyun Kim, co-founder and CEO of Superb AI, a startup building tools to help companies manage data across the entire machine learning application lifecycle. This includes tools to label, store, and monitor data assets that power all computer vision...
Guest Post: “ML Data”: The Past, Present, and Future
In this article, co-founder and CTO of Galileo Atindriyo Sanyal gives a fascinating overview of the ‘ML data intelligence’ evolution and shares a few insights on why the organizations that obsess on their ML data quality will quickly greatly outperform those that...
High-quality data meets enterprise MLOps
According to the 2021 enterprise trends in machine learning report by Algorithmia, 83% of all organizations have increased their AI/ML budgets year-on-year, and the average number of data scientists employed has grown by 76% over the same period. However, the process...
Resilient human-in-the-loop pipelines with Pachyderm and Toloka
Why data prep is hard Many data scientists and machine learning teams report that they spend about 80% of their time preparing, managing, or curating their datasets. There are three things that have enabled the ML revival over the last 5–10 years: breakthroughs in...
Buy or Build for ML Ops
Where to get started? Let’s start with admitting that there is no 100% waterproof pros and cons list of buying vs building an ML Ops stack that works for everyone. It always depends on your specific situation. In this article, I try to highlight some of the most...
How to convert DMatrix to NumPy format for your machine learning model?
Miles to kilometers, celsius to Fahrenheit, and Google Docs to word are just some of the conversions we all do in our day-to-day life. When we fly overseas (hello pre-COVID days) or work with a company with different sharing methods, we often find ourselves converting...
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