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
Data-driven Retraining with Production Observability Insights
We all know that our model’s best day in production will be its first day in production. It’s simply a fact of life that over time model performance degrades. ML attempts to predict real-world behavior based on observed patterns it has trained on and learned. But the...
MLOps Beyond Training: Simplifying and Automating the Operational Pipeline
The Evolving Meaning of ‘MLOps’ When you say ‘MLOps’, what do you mean? As the technology ecosystem around ML evolves, ‘MLOps’ now seems to have (at least) two very different meanings: One common usage of ‘MLOps’ refers to the cycle of training an AI model: preparing...
8 Reasons to version control your database
Version control is synonymous with software development. Most will automatically think of GitHub. Those superpowers gifted to software developers have made application builds quicker, more efficient, and more collaborative. The database world has been slow to follow....
Three Takeaways From Our Survey Of Top ML Teams
This blog highlights findings from Arize AI’s recent survey of ML teams. To see the full results, download a copy of the report. Compared to DevOps or data engineering, MLOps is still relatively young as a practice despite tremendous growth. While it’s tempting to...
High-quality data meets enterprise MLOps
According to the 2021 enterprise trends in machine learning report by Algorithmia, 83% of all organizations have increased their AI/ML budgets year-on-year, and the average number of data scientists employed has grown by 76% over the same period. However, the process...
8 Concept Drift Detection Methods
There is a wide range of techniques that can be applied for detecting concept drift. Becoming familiar with these detection methods is key to using the right metric for each drift and model. In the article below, I review four types of detection methods: Statistical,...
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