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
Part 1: An Overview of DataOps For Computer Vision
Computer vision applications, in specific, and machine learning applications, in general, rely heavily on data to train the models. In production systems, input data is fed into the model to make inferences. These production systems then use the inference outputs as...
When I Drift, You Drift, We Drift
In the latest edition of “The Slice,” a blog series from Arize that explains the essence of ML concepts in a digestible question-and-answer format, we dive into different types of drift – including concept drift vs data drift. When data science and engineering teams...
Explain This – Beyond Lime and SHAP: the Fastest Approach to AI Explainability
Learn how a novel approach to explainability based on adversarial machine learning can be used to explain the predictions of deep neural networks, powered by a single GPU Tesla V 100-SXM2 to produce better results faster than LIME and SHAP. This talk covers our...
A Primer on Data Labeling Approaches To Building Real-World Machine Learning Applications
Introduction In computer vision and machine learning operations, data labeling is an essential part of the overall workflow. For reference, data labeling is the process by which raw images, video, or audio files are identified and annotated individually for machine...
Getting Started with TerminusDB using the Python Client
Our Developer Relationship Associate, Cheuk Ting Ho, recently put together this six-part tutorial to demonstrate how to get started with TerminusDB and TerminusX using the Python client. The six videos detail everything you need to know about working with TerminusDB...
Handling Large Datasets in Data Preparation & ML Training Using MLOps
Operationalizing ML remains the biggest challenge in bringing AI into business environments Data science has become an important capability for enterprises looking to solve complex, real-world problems, and generate operational models that deliver business value...
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