Learn how to set up and orchestrate end-to-end ML pipelines, leveraging large DGX clusters. We'll demonstrate how to orchestrate your training and inference workloads on DGX clusters, with optional setup of remote development environments leveraging the multi-instance GPUs on the NVIDIA A100. We'll also show how pipelines can be built to serve both research and deployment workloads, all while leveraging the compute inherent in the DGX cluster.

Learn how to structure a data scientist-first orchestration setup that allows your DS team to self-manage their allocated NVIDIA GPU clusters, without needing continuous hand-holding from DevOps/IT. We'll demonstrate this setup while using NVIDIA Clara Train SDK to walk through best practices in orchestration, experiment management, and data operations and pipelining. While examples will be health-care-focused, the concepts demonstrated are agnostic to any ML/DL use case in any industry.

Learn how to take models from research into deployment in an efficient and scalable manner. We'll demonstrate workflows and methodologies so that your data science team can make the most of their NVIDIA hardware systems and software tools (including TRITON!).

Whether you are a veteran Data Science practitioner, a novice ML engineer, or a hard-working DevOps ninja, you probably heard about MLOps. But what are MLOPs? And do they only relate to applied machine learning in production?

ClearML is an industry leading MLOps suite, fully open source and free in the best sense. Designed to ease the start, running and management of experiments and orchestration for every day practitioners, we will also see how it provides a clear path to deployment. Starting with a high level overview of the parts built into ClearML, we will then journey into what is and also, importantly, what is not part of ClearML's mandate. Along the way we will demonstrate how-to integrate into your PyTorch code, as well as the capabilities of reporting and possible workflows that could be made easier by pipeline usage.

Ariel (ft. G. Raffa) discusses the reasoning behind model stores, why you might want to build one, and reviews a model store library vs. ClearML to understand what needs to be built "on top" of our open-source MLOps Engine. + Operator AI ClearML is the only open-source tool to manage all your MLOps in a unified and robust platform providing collaborative experiment management, powerful orchestration, easy-to-build data stores, and one-click model deployment.