AI INFRASTRUCTURE ALLIANCE
Building the Canonical Stack for Machine Learning
Our Work
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 showing how different components of a complete enterprise machine learning stack can and should interoperate. We deliver essential reports and research, virtual events packed with fantastic speakers and visual graphics that make sense of an ever-changing landscape.
Download the Enterprise Generative AI Adoption Report
Oct 2023
Our biggest report of the year covers the wide world of agents, large language models and smart apps. This massive guide dives deep into the next-gen emerging stack of AI, prompt engineering, open source and closed source generative models, common app design patterns, legal challenges, LLM logic and reasoning and more.
Get it now. FREE.
AI Landscape
Check out our constantly updated AI Landscape Graphic that shows the full range of capabilities for major MLOps tools instead of just pigeonholing them into a single box that highlights only one aspect of their primary characteristics.
Today’s MLOps tooling offers a broad sweep of possibilities for data engineering and data science teams. You can’t easily see those capabilities in typical graphics that show a bunch of logos so we’ve engineered a better info-graphic to let you quickly figure out if a tool does what you need now.
Events – Past and Future
Check here for our upcoming events and to watch videos from past events. We put on 3 to 4 major events every year and they’re packed with fantastic speakers from across the AI/ML ecosystem.
MEMBERS
ARTICLES
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...
Understanding Monitoring, Observability and Explainability in AI/ML and Why They’re Three Different Things
Monitoring has a long history in IT, with multiple companies and open source projects delivering robust tools that keep the pulse of your IT infrastructure so your systems stay running strong. But how do you monitor AI/ML models in production? You might think it’s...
Automating MLOps for Deep Learning: How to Operationalize DL With Minimal Effort
Operationalizing AI pipelines is notoriously complex. For deep learning applications, the challenge is even greater, due to the complexities of the types of data involved. Without a holistic view of the pipeline, operationalization can take months, and will require...
MLOps in 10 Minutes
How MLOps helps across all stages of ML project It’s a common misconception that MLOps is solely about the tools we use for deploying models and preparing the infrastructure for it. Partly it is, but it’s not the whole story — there’s much more. In this post, I’ll...
How Can I Measure Data Quality?
Flag all your data quality issues by priority in a few lines of code “Everyone wants to do the model work, not the data work” — Google Research According to Alation’s State of Data Culture Report, 87% of employees attribute poor data quality to why most organizations...
Debugging Python-Based Microservices Running on a Remote Kubernetes Cluster
with VS Code and Bridge to Kubernetes At Modzy we’ve developed a microservices based model operations platform that accelerates the deployment, integration, and governance of production-ready AI. Modzy is built on top of Kubernetes, which we selected for its...
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