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
Contextual Relevance in Ad Ranking
In this talk we’ll look at why contextual relevance in ad ranking and ad targeting is a must-have for a successful ad campaign; what are the current state-of-the-art machine learning solutions in the field of contextual advertising and ad ranking; and how...
Beyond Monitoring: The Rise of Observability
By: Aparna Dhinakaran & Barr Moses As data and machine learning ecosystems become increasingly complex and companies ingest more and more data, it’s important that data and ML engineering teams go beyond monitoring to understand the health of their data-driven...
ML Talks: Ari Kamlani: Senior AI Solutions Architect
Tell us a bit about yourself, your background, where you work, and what you do there. My journey is pretty varied and spans a couple of different areas, structured across a few different types of organizations, industry domains, and technologies. I’ve worked across...
Drift Metrics: How to Select the Right Metric to Analyze Drift
In our last post we summarized the problem of drift in machine learning deployments (“Drift in Machine Learning: Why It’s Hard and What to Do About It” in Towards Data Science). One of the takeaways from the article is: methods for dealing with drift must identify...
The Key to Sustainable AI is MLOps
Practical Green MLOps A manifesto on environmentally sustainable AI infrastructure When we consider the environmental impact of AI and MLOps, our attention is usually focused on the significant energy usage for the compute cycles required for training. After all, a...
Data Reliability Automated with PipeRider
tl;dr PipeRider is an open-source data reliability toolkit for identifying data quality issues across pipelines. PipeRider was created after months of industry research and it’s available now. Start learning more about the quality of your data by taking PipeRider for...
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