Avinash Singh
A DevOps and Platform engineering professional with over a decade of experience in CI/CD, Hybrid Cloud Infrastructure, Kubernetes and cloud native development.
Avinash is passionate about process optimisations using open source automations and AI/ML.
As a keen contributor and advocate of open source community, his recent interests are in MLOps and Sustainable computing area.
Red Hat
Principal Software Engineer
Session
AI models have become the next wave of cloud native applications. And, in a cloud native world, containers have become the de facto method of delivery. However, instead of bundling the model inside a container, what if there was a way to publish models directly while reusing many of the same technologies?
OCI artifacts have emerged as this solution and an increasing number of technologies have adopted this approach for packaging and distributing content. When considering OCI artifacts for AI models, several questions still remain open.
Packaging machine learning models is complex, often requiring teams to use proprietary package types or cobble together open source tools. These inconsistent environments, manual processes, and proprietary formats lead to deployment failures, delays, increased operational costs, and vendor lock-in.
ModelPack, an emerging Open Source Project, solves these challenges by providing a standardized, consistent, reproducible, and portable packaging format for AI/ML models, that is vendor neutral. The result simplifies deployment, reduces errors, and ensures models work seamlessly across a variety of environments.
In this session, attendees will learn how to package, distribute and run AI/ML projects as OCI artifacts like a pro. By exploring the end to end lifecycle of an AI/ML model including the resources provided by the ModelPack project, attendees will not only see the benefits, but have a repeatable process that they can reuse in their own environments.