RAPIDS supercharges data science with NVIDIA accelerated compute. Paired with Dask, data professionals can build highly-performant, distributed workloads with a comfortable toolset similar to favorites like pandas or scikit-learn. In this workshop, we’ll discuss how Dask + RAPIDS empower practitioners, how to start with these tools quickly, and how they’re used to solve common challenges.
Large-scale data science is a complex and computationally-intensive undertaking. As datasets continue to grow, data science tools have evolved to better handle big data use cases. Dask makes working with big data smooth through a comfortable experience, similar to pandas or scikit-learn, that simplifies distributed computing.
Driving the dramatic advancement of deep learning, GPUs have shown the same promise for traditional data science and machine learning workloads. RAPIDS aims to accelerate end-to-end data science with NVIDIA GPUs through a plethora of highly-accessible libraries for data preparation, analysis, and visualization. When paired together, RAPIDS complements the usability of Dask with the power of NVIDIA accelerated compute, helping to reduce iteration time, drive more accurate insights, and decrease the cost and overhead of large-scale data science.
To better familiarize data practitioners with accelerated data science, this workshop will delve into what RAPIDS is and how it works with Dask. We'll discuss how Dask + RAPIDS empower practitioners, how to get started with these tools quickly, and how they’re used in innovative enterprises. By the end of the workshop, attendees will understand how accelerated data science can help them solve their biggest challenges.