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
ARTICLES
Machine Learning Monitoring: why it matters and how to get it right
Avoid these common ML monitoring mistakes – your model’s success hangs in the balance. So you’ve built a machine learning model that works well in the lab. You’ve validated it, gotten the green light from the internal stakeholders, ensured that it met any regulatory...
Fix your models by fixing your Datasets
By: Atindriyo Sanyal, Vikram Chatterji, Nidhi Vyas, Ben Epstein, Nikita Demir, Anthony Corletti Abstract The quality of underlying training data is very crucial for building performantmachine learning models with wider generalizabilty. However, current machinelearning...
What is MLOps?
Machine learning (ML) becomes effective once models are in production. Organizations, on the other hand, usually underestimate the complexity and challenges of implementing machine learning in production, devoting the majority of their resources to ML development and...
Designing APIs for AI
It’s estimated that anywhere from 50-90% of AI models developed never make it past the AI “valley of death” that exists between the lab and production deployment. This tech talk covers how an API-based approach to building and maintaining AI-enabled applications can...
A Modern Approach to Versioning Large Files for Machine learning and more
Original article (Mandarin) written by ArtiV developer Chen-en Lu (Popcorny) for the MLOps Taiwan Facebook community. Translated and adapted to English by Dave Flynntl;dr ArtiV is smart version control system for large files, especially suited for use on machine...
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:...
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