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
How to use Duplicate Detection to create Unique Entries
What happens when you have two records which are really meant to be one? Most people with a cell phone have encountered this problem with duplicate contacts. The problem of recognizing them is tricky, but when you find the duplicate, you’ve got another problem:...
Zillow Offers: A Case for Model Risk Management
In the past three years, Zillow invested hundreds of millions of dollars into Zillow Offers, its AI-enabled home-flipping program. The company intended to use ML models to buy up thousands of houses per month, whereupon the homes would be renovated and sold for a...
What I learned about ML infra in the last 2 years
Machine Learning Infrastructure vs traditional IT Infrastructure 2 years ago I had heard of DevOps and knew some bits and pieces of how AI works, such as: you take data from somewhere, train a model when you’re satisfied you feed it new data and work with the...
5 Principles You Need To Know About Continuous ML Data Intelligence
In this article, founder and CEO of Galileo Vikram Chatterji discusses the problems with ML data blindspots and introduces ML Data Intelligence that helps an ML team holistically understand and improve the health of the data powering ML across the organization. As a...
Top 10 Open-Source Data Science Tools in 2022
I’m not going to list Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow, PyTorch, etc. You probably know about these already. There is nothing wrong with these libraries; they’re already the bare minimum essential for data science using python. And the...
Using Snowflake and Dask for Large-Scale ML Workloads
Many organizations are turning to Snowflake to store their enterprise data, as the company has expanded its ecosystem of data science and machine learning initiatives. Snowflake offers many connectors and drivers for various frameworks to get data out of their cloud...
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