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
Maximizing ML Infrastructure Tools for Production Workloads – Arize AI
Businesses in almost every industry are rapidly adopting Machine Learning (ML) technology and understanding the various platforms and offerings can be a challenge. The ML Infrastructure space is crowded, confusing, and complex.
Powering Up Your ML Monitoring in Production Now
Today's enterprises rely on machine learning-powered predictions to guide business strategy, such as by forecasting demand and mitigating risk. For an increasing number of businesses, machine learning (ML) underpin their core business model, like financial...
AI Alliance Member Tecton Secures $35M in Series B Funding and Delivers Their Feature Store to the World
Today marks a big milestone for Tecton. We’re excited to announce that the Tecton feature store is now in General Availability, and that we have raised $35 Million in Series B funding from our existing lead investors, Andreessen Horowitz and Sequoia, to fuel our next...
What is Data Lineage?
What is data lineage? At its most basic it’s the history of your data. It tells us where that data comes from, where it lives and how it’s transformed over time. As AI teams pour over data looking for patterns, or process it and massage it to get it into shape for...
Fire Up Data Logging Across Your ML Systems with WhyLabs
Fire up your MLOps with a scalable, lightweight, open source data logging library Co-author: Bernease Herman We are thrilled to announce the open-source package whylogs. It enables data logging for any ML/AI pipeline in a few lines of code. Data logging is a critical...
Accelerate your Hyperparameter Optimization with PyTorch’s Ecosystem Tools
The design and training of neural networks are still challenging and unpredictable procedures. The difficulty of tuning these models makes training and reproducing more of an art than a science, based on the researcher’s knowledge and experience. One of the reasons...
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