Pre Event Workshop | Lightning fast MLOps: Accelerating the Machine Learning lifecycle with Nvidia and Domino Data Lab

Domino, in partnership with NVIDIA, supports open, collaborative, reproducible model development, training, and management free of DevOps constraints. In this workshop we present the architecture of a joint solution, which enables data science professionals to easily access GPU-accelerated compute and frameworks for the purposes of data processing / feature engineering, model training, and model deployment. We talk about what an end-to-end ModelOps life cycle looks like and what the key challenges are. We teach basic paradigms around using containerised IDEs (JupyterLab, RStudio, VScode), accelerating workloads via GPU-Optimized Nvidia containers, and taking containerised models to production. We cover RAPIDS – a set of open-source software libraries which gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. We also talk about scalability from workstation and cluster to cloud and HPC.

• Learn to spin up containerised IDEs powered by elastic backend compute
• Learn how to spin up distributed clusters on-demand (Spark/Ray/Dask)
• See on-demand distributed and GPU-accelerated compute in action (Spark with Nvidia RAPIDS Accelerator for Apache Spark) 
• Containerise and take a model to production in under 5 minutes
• Learn the core tools to use RAPIDS for everyday data science.
• Build the foundations for you to learn RAPIDS capabilities now and in the future.

• Attendees should have basic machine learning knowledge and Python skills
• Attendees need to bring their own laptop so they can access the system used for hands-on activities
• Familiarity with Jupyter notebook environment

————————————————————————————————————————————————————

Nikolay Manchev – Head of Data Science for EMEA | Domino Data Lab

Nikolay is an experienced Data Science professional who currently leads the EMEA Data Science team at Domino Data Lab.  He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King’s College London. His area of expertise is Statistics, Mathematics, and Data Science in general, and his research interests are in Neural Networks with emphasis on biological plausibility. He writes articles and blogs regularly and speaks at various conferences (ODSC, Big Data Spain, Strata, Big Data London etc.) to build awareness about data science and artificial intelligence. He is also the organizer of the London Data Science and Machine Learning meetup and recipient of several technical mastery awards like the Oracle ACE Award and the IBM Outstanding Technical Achievement Award.

Abubakr Karali – Senior Solutions Architect | NVIDIA

I am Senior Solutions Architect for intelligent video analytics at Nvidia. I support the computer vision and machine learning activities across EMEA, helping our partners and customers developing, optimizing and deploying their computer vision system. Before NVIDIA, I spent a couple of years at Microsoft Research and Research Institute of Sweden. Which in all resulted into 9 publications, one patent and one startup.