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 Improve Data Labeling Efficiency with Auto-Labeling, Uncertainty Estimates, and Active Learning
In this post we'll dive into the machine learning theory and techniques that were developed to evaluate our auto-labeling AI at Superb AI. More specifically, how our data platform estimates the uncertainty of auto-labeled annotations and applies it to active learning....
How the Canonical Stack for Machine Learning will Unleash the Next Generation of Cutting Edge AI Apps
The rise of the canonical stack in machine learning will change the way every company builds and develops AI/ML apps in the future, making it easier and faster to get started with advanced data science. Now companies will no longer need to build their own infrastructure from scratch. They can get right to building cutting edge models fast.
The MLOps Stack
What is MLOps (briefly) MLOps is a set of best practices that revolve around making machine learning in production more seamless. The purpose is to bridge the gap between experimentation and production with key principles to make machine learning reproducible,...
What is a Feature Store
Data teams are starting to realize that operational machine learning requires solving data problems that extend far beyond the creation of data pipelines. In Tecton’s previous post, Why We Need DevOps for ML Data, we highlighted some of the key data challenges that...
A Comprehensive Overview of Natural Language Processing by the Algorithmia team
Everything you need to know about Natural Language Processing.
12 Steps to Reproducible Machine Learning in Production
The last two decades have yielded us some great understandings about Software Development. A big part of that is due to the emergence of DevOps and it’s wide adoption throughout the industry. Leading software companies follow identical patterns: Fast iterations in...
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