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
How to set up an ML Data Labeling Pipeline: Best Practices and Examples
Supervised Machine Learning projects typically require labeled data to train the algorithms. You want to use high-quality data that corresponds to the problem you are trying to solve. But how do you obtain this kind of data? In this session, Magdalena Konkiewicz shows...
What’s the difference: JSON diff and patch
What will the distributed data environment in Web3 look like? How will we have a distributed network of data stores which allow updates and synchronizations? What is it that allows git to perform distributed operations on text so effectively? Is it possible to do the...
Best Practices in ML Observability for Monitoring, Mitigating and Preventing Fraud
Every year, fraud costs the global economy over $5 trillion. In addition to taking a deeply personal toll on individual victims, fraud impacts businesses in the form of lost revenue and productivity as well as damaged reputation and customer relationships. AI...
5 Ways to Prevent Data Leakage Before it Spills Over to Production
Data leakage isn’t new. We’ve heard all about it. And, yes, it’s inevitable. But that’s exactly why we can’t afford to ignore it. If data leakage isn’t prevented early on it ends up spilling over into production, where it’s not quite so easy to fix. Data leakage in...
Getting in Shape with a Raspberry Pi, an OAK-1 and ClearML
Locking your screen from a raspberry pi and only unlocking it again when you did enough pushups, what a world we live in. The whole solution in action This is the more in-depth, accompanying blogpost of this youtube video, go check it out first if you haven’t already....
Understanding Bias & Fairness in Machine Learning
Machine learning and big data are becoming ever more prevalent, and their impact on society is constantly growing. Numerous industries are increasingly reliant on machine learning algorithms and AI models to make critical decisions that impact both business and...
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