AI INFRASTRUCTURE ALLIANCE
Building the Canonical Stack for Machine Learning
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 establishing clean APIs, integration points, and open standards for how different components of a complete enterprise machine learning stack can and should interoperate. That lets organizations make better decisions about the tools they’ll deploy in the AI/ML application stacks of today and tomorrow.
About
OUR MISSION
The AI Infrastructure Alliance’s mission is to help organizations:
1) Establish a canonical stack for Artificial Intelligence (AI) and Machine Learning (ML) Operations (MLOps)
2) Develop ideal best practices and architectures for doing AI/ML at scale in enterprise organizations
3) Foster openness for algorithms, tooling, libraries, frameworks, models and datasets in AI/ML
4) Advocate for technologies, such as differential privacy and homomorphic encryption, that helps anonymize data sets and protect privacy
5) Work towards universal standards to share data between AI/ML applications
MEMBERS
ARTICLES
ML Testing and Debugging – The Missing Piece in AI Development
Today, TruEra launched TruEra Diagnostics 2.0, the next major release of our flagship solution. This release is a game-changer for our customers and for ML leaders looking to accelerate the time required to get high quality models into production. That’s because...
ML Monitoring in Under 5 Minutes
It only takes a few minutes and a few lines of code to monitor your ML models and data pipelines. Data validation and ML model monitoring are foundational steps to building reliable pipelines and responsible machine learning applications. In this short post, I will...
Move Fast Without Breaking Things in ML
Many companies are learning that bringing a model that works in the research lab into production is much easier said than done. Written by Bob Nugman, ML Engineer at Doordash, and Aparna Dhinakaran, CPO of Arize AI. In this piece, Bob and Aparna discuss the importance...
How to Debug Transfer Learning Drift for Tabular Models
In a previous article, we analyzed a model for predicting Airbnb listing prices in San Francisco. The model was an XGBoost model trained on Airbnb data scraped by Inside Airbnb and hosted by OpenDataSoft. In this article, we’ll take a step further into the model’s...
6 examples of TerminusDB’s enterprise data solutions
TerminusDB is a powerful in-memory document graph database and provides several enterprise data solutions. It’s packed with features and sometimes the ability to fit into a wide array of applications causes confusion and ambiguity. This article aims to provide a...
How to go from raw data to production like a pro
An odyssey on improving data quality with synthetic data and model delivery with MLOps Machine Learning and AI are two concepts that definitely have changed our way of thinking in the last decade, and will probably change even more in the next few years. But, we are...
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