Modzy Joins the AI Infrastructure Alliance

The Alliance and its members seek to bring clarity to this fast-evolving field building the future of AI and ML.

Modzy Joins the AI Infrastructure Alliance

We’re thrilled to announce that we’ve joined the AI Infrastructure Alliance (AIIA), a non-profit consortium focused on establishing standards and interoperability for AI and machine learning. We join 30+ industry leaders building the canonical stack for AI and machine learning development.

The canonical stack for machine learning

Data scientists and developers require a diverse mix of tools to build and maintain AI-powered solutions. This includes different types of tools for:

  • Data gathering and preparation
  • Experimenting, training, tuning, and testing
  • Productionization, deployment, and inference
  • Monitoring, auditing, management, and retraining

The Alliance and its members seek to bring clarity to this fast-evolving 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.” This gives organizations a complete picture of how they might leverage different tools to build and maintain the right AI stack for their teams’ current and future needs.

Modzy and the alliance

As an open architecture solution designed to integrate with a broad ecosystem of tools already used today by data science and development teams, we’re excited about the opportunity to work with the other experts in the space to shape the future of how teams deploy and maintain their AI-enabled systems. The Modzy model operations platform accelerates the deployment, integration, and governance of production-ready AI, helping teams to run AI at scale anywhere, including at the edge. We’ve also contributed to two exciting open-source projects, the Open Model Interface, which provides a specification for multi-platform OCI-compatible container images for ML models, and chassis.ml, which builds models directly into DevOps-ready container images for inference. We look forward to a successful partnership with other industry leaders to shape the future of the canonical stack for AI and machine learning.