Dmitry Mironov is an AI Solutions Architect at NVIDIA. He helps customers use the GPUs efficiently and helps speed up various pipelines in CV, NLP, Conversational AI, and Data Science. Before NVIDIA, Dmitry served as a CTO and co-founder of a startup. He had been integrating Computer Vision into gold mining, transportation, energy, and other industries.
This hands-on tutorial will teach you how to accelerate every component of a machine learning system and improve your team’s productivity at every stage of the ML workflow. You’ll learn how to get started with RAPIDS and NVIDIA Forest Inference Library, and how to go beyond the basics to get the most out of your accelerated infrastructure. We’ll do all of this in the context of a real-world application that models financial payments fraud and detects it in real-time. We’ll show you how: RAPIDS enables you to find better insights into your data more quickly, through accelerated visualization techniques RAPIDS Machine Learning models can outperform rules-based approaches to detecting payments fraud NVIDIA Forest Inference Library enables you to accelerate inference of tree models, scoring incoming transactions with high throughput and low latency Data scientists will experience the high-velocity exploratory workflows enabled by NVIDIA RAPIDS and learn how to best take advantage of GPUs when porting CPU-based pandas and scikit-learn code to run on RAPIDS. Application developers and IT ops professionals will learn more about data science workflows, see how real-world ML systems work, and learn about the myriad benefits of GPU acceleration for these systems and the teams who build them. The tutorial can be delivered both remotely and onsite. Attendees would need a laptop and a stable internet connection. Attendees to the tutorial will be provided with a url to access the lab environment, so that they can access and run the tutorial with no prior set-up required. Familiarity with standard Python code is desirable.