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
The MLOps Stack is Missing A Layer
Deploying ML at scale remains a huge challenge. A key reason: the current technology stack is aimed at streamlining the logistics of ML processes, but misses the importance of model quality. Machine Learning (ML) increasingly drives business-critical...
Test your data quality in minutes with PipeRider
tl;dr If you missed out on PipeRider’s initial release, then now is a great time to take it for a spin. Data reliability just got even more reliable with better dbt integration, data assertion recommendations, and reporting enhancements. PipeRider is open-source and...
Improving Your ML Datasets, Part 2: NER
In our first post, we dug into 20 Newsgroups, a standard dataset for text classification. We uncovered numerous errors and garbage samples, cleaned about 6.5% of the dataset, and improved validation by 7.24 point F1-score. In this blog, we look at a new task: Named...
A Data Scientist’s Guide to Identify and Resolve Data Quality Issues: Doing this early for your next project will save you weeks of effort and stress
If you've worked in the AI industry with real-world data, you’d understand the pain. No matter how streamlined the data collection process is, the data we’re about to model is always messy. According to IBM, the 80/20 Rule holds for data science as well. 80% of a data...
Scaling Breast Cancer Detection with Pachyderm
Introduction Breast cancer is a horrible disease that affects millions worldwide. In the US and other high-income countries, advances in medicine and increased awareness have significantly improved the survival rate of breast cancer to 80% or higher. However, in many...
Concept drift in machine learning 101
As machine learning models become more and more popular solutions for automation and prediction tasks, many tech companies and data scientists have adopted the following working paradigm: the data scientist is tasked with a specific problem to solve, they receive a...
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