Explore a multi-GPU, RAPIDS/BlazingSQL-based interactive analysis tool with a Jupyter Notebook interface that provides a major speedup for querying and analyzing datasets compared to conventional, persistent databases. This pipeline is the result of an intense collaboration between Oak Ridge National Laboratory, Scripps Research, NVIDIA, and BlazingSQL and represents a new open-science capability for massively parallel drug discovery in response to a public health crisis.

Learn how to leverage well-defined APIs to build custom machine learning estimators that scale using Dask and RAPIDS. Create drop-in replacements for ensembles and pipelines that work with existing estimators from scikit-learn, Dask-ML, XGBoost, and RAPIDS cuML. Learn from Capital One use cases on what’s working.

Learn about our journey of building real-time streaming analytics pipelines for NVIDIA^® GeForce NOW^™ Cloud Gaming using cuStreamz. Deep dive into the cuStreamz architecture on how we built production-grade streaming features like checkpointing, state management, and accelerated source and sink connectors.