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
Building Great APIs for an AI-Powered Future
Modzy is an Artificial Intelligence (AI) platform designed to bridge the gap over what we call the “AI Valley of Death”, or the often difficult process of getting an AI or machine learning (ML) model out of the lab and into production in a secure and scalable way....
Using Statistical Distance Metrics for Machine Learning Observability
Statistical Distances are used to quantify the distance between two distributions and are extremely useful in ML observability. This blog post will go into statistical distance measures and how they are used to detect common machine learning model failure modes. Data...
Part 2: Three DataOps Challenges That Most Computer Vision Teams Struggle With
This is a 3 part series. To view Part 1 click here. Introduction Implementing state-of-the-art architectures, tuning model hyperparameters, and optimizing loss functions are the fun parts of machine learning. Sexy as it may seem, behind each model that gets deployed...
How to build a React backend with TerminusX
In this video tutorial, Cheuk shows you how to build a React backend for a simple content management system. The full tutorial with accompanying files can be found at: Why React? React is a very popular JavaScript library for building user interfaces. In the 2021...
Synthetic Data with Gumbel-Softmax Activations
Photo by v2osk on Unsplash In classification a central problem is how to effectively learn from discrete data formats (categorical or ordinal features). Most datasets present us with this problem so I guess it is fair to say that almost all data scientists have...
Concept Drift Deep Dive: How to Build a Drift-Aware ML System
In a world of turbulent, unpredictable change, we humans are always learning to cope with the unexpected. Hopefully, your machine learning business applications do this every moment, by adapting to fresh data. In a previous post, we discussed the impact of...
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