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In this talk, we present our philosophy for modern, no-nonsense data pipelines, highlighting the advantages of a PaaS approach, and showing (with open, freely available code) how the entire toolchain works on real-world data with realistic constraints. It is indeed a wonderful time to build machine learning systems, as we don’t have much to do anymore! Thanks to a growing ecosystem of tools and shared best practices, even small teams can be incredibly productive at “reasonable scale”. Guy speaks at conferences and meetups around the world, writes blog posts and delivers webinars. He runs POCs and technical projects for our commercial and enterprise customers, including on-site installations and workshops. Guy Salton is the Solutions Engineering Lead at Run:AI, specializing in the fields of DevOps, Cloud Computing, Kubernetes, Containers, Virtualization, CI/CD and AI computing.
TELECHARGER LA VERSION FREE TELENAV SUR MON APP WORLD HOW TO
How to use MPS (Multi-Process Services) with fractional GPU to increase throughputĭynamic MIG functionality – how to get dynamic MIG slices for each new job when using the NVIDIA A100 GPUĪdditional capabilities to run Inference on top of Kubernetes There are very few organizations using the new NVIDIA MIG functionality successfully, so even if you’re not using A100s yet, this is a unique opportunity to see how the MIG works for inference use cases. We’ll discuss using fractional GPU capabilities to improve throughput and reduce latency, and we’ll show how one organization built an inference platform on top of Kubernetes with the NVIDIA A100 MIG to support their scaling AI initiatives.
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In this workshop applicable to both Data Scientists and ML Engineers, Guy Salton will give an overview of the challenges in moving ML prototypes to production, and how best-in-class ML teams are successfully overcoming these hurdles. In the ML lifecycle, inference workloads present a critical challenge, where throughput and latency become key measures and teams struggle to meet efficient GPU utilization levels. MLOps hurdles don’t end after models are pushed to production. Her research interests lie in multilingual computing and the relevance of NLP beyond research both in industry practice as well as in other disciplines, through inter-disciplinary research. She recently co-authored a book: “Practical Natural Language Processing: A Comprehensive Guide to Building Real World NLP Systems”, published by O’Reilly Media (June, 2020), which was also translated into Chinese.
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She has worked in the area of Natural Language Processing (NLP) over the past decade in various roles – as a software developer, researcher, educator, and a senior data scientist. Sowmya Vajjala currently works as a researcher in Digital Technologies at National Research Council, Canada’s largest federal research and development organization. – Data Augmentation in the Context of NLP. – Different Ways of Collecting Labeled Data We may often end up in scenarios where we have a problem, and a way to solve it, but no dataset to start working on the solution! In this workshop, I will introduce some ways of approaching this problem, such as looking for existing datasets, data annotation, automatic data labeling, data augmentation, and transfer learning. However, in many real-world scenarios, we don’t have that luxury of already having a labeled dataset.
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NLP tutorials and workshops typically start with a labeled/annotated dataset, and discuss different ways of representing text/building models.