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
Accelerate Machine Learning with a Unified Analytics Architecture
Book description Machine learning has accelerated in several industries recently, enabling companies to automate decisions and act based on predicted futures. In time, nearly all major industries will embed ML into the core of their businesses, but right now the gap...
Putting together a continuous ML stack
Due to the increased usage of ML-based products within organizations, a new CI/CD like paradigm is on the rise. On top of testing your code, building a package, and continuously deploying it, we must now incorporate CT (continuous training) that can be stochastically...
Hardware Accelerators for ML Inference
There are many different types of hardware that can accelerate ML computations - CPUs, GPUs, TPUs, FPGAs, ASICs, and more. Learn more about the different types, when to use them, and how they can be used to speed up ML inference and the performance of ML systems.This...
The Playbook to Monitor Your Model’s Performance in Production
As Machine Learning infrastructure has matured, the need for model monitoring has surged. Unfortunately this growing demand has not led to a foolproof playbook that explains to teams how to measure their model’s performance. Performance analysis of production models...
How ML Model Testing Accelerates Model Improvement – Using Test-Driven Modeling for Fire Prediction
Motivation In the United States since 2000, an average of 70,072 wildfires burned an average of seven million acres per year. This rate doubles the average annual acreage burned in the 1990s (3.3 million acres). In just 2021, nearly 6,000 structures burned, sixty...
Practical Data Centric AI in the Real World
Data-centric AI marks a dramatic shift from how we’ve done AI over the last decade. Instead of solving challenges with better algorithms, we focus on systematically engineering our data to get better and better predictions. But how does that work in the real world?...
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