This hands-on lab will teach you how to accelerate each 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 Triton Inference Server, 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 Triton Inference Server enables you to accelerate inference, scoring incoming transactions with high throughput and low latency
Data scientists will see 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 how real-world ML systems work and see the myriad benefits of GPU acceleration for these systems and the teams who build them.