Data Science in 30 Minutes: Accelerating Data Science Workflows with Bartley Richardson

bartleywebfinal-1-300x150-1

GPUs built on CUDA have been used for deep-learning and other applications for a long time. But, when you look at data scientists and they work they’re doing, CUDA doesn’t really fit well into their workflow. Today’s scientists want quick exploration, quick results and to be able to shift gears without interrupting their train of thought. They want to think at the speed of data.

In this webinar, Bartley Richardson, PhD, a former fellow of The Data Incubator and a senior data scientist at Nvidia, addresses this issue. Richardson shares Nvidia RAPIDS project, an open-source suite of data processing and machine learning libraries that enables GPU acceleration for data science workflows. It also delivers a 50- to 100-times improvement over traditional GPU processing, but still using the same code and following the APIs that data scientists are familiar with (e.g., Pandas, SciKit).

Related Blog Posts

Rainbow Pride Keyboard

Data Science & The LGBTQIA+ Community

Data science is the backbone of informed decision-making in companies. It is a discipline that gathers, analyzes, and makes sense of large data sets. Data science is a large field that encompasses a wide range of tasks and those on a data science career path need to be versed in many areas, so let’s dig deeper into the 4 most important aspects of data science.

Read More »
man working with data

The 4 Important Aspects of Data Science

Data science is the backbone of informed decision-making in companies. It is a discipline that gathers, analyzes, and makes sense of large data sets. Data science is a large field that encompasses a wide range of tasks and those on a data science career path need to be versed in many areas, so let’s dig deeper into the 4 most important aspects of data science.

Read More »