Online or onsite, instructor-led live RAPIDS training courses demonstrate through interactive hands-on practice how to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms, such as XGBoost, cuML, etc.
RAPIDS training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Nagoya onsite live RAPIDS trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Nagoya is the third largest city in Japan. This open office is located in the district surrounding Nagoya station. Since 2015...
Nagoya is the third largest city in Japan. This open office is located in the district surrounding Nagoya station. Since 2015, landmark buildings kept inaugurating around the station and by 2017, it is predictable that many corporations will choose to be in this Meieki area. Furthermore, with the magnetic-levitated linear shinkansen to start service in 2027, Nagoya has strategic advantage.
Nagoya and Aichi area is known for its auto industry like Toyota. Besides automobiles, there are many head offices of Japanese manufacturers. With their affiliated companies and partners located in the same area, the city itself is a business complex. In recent years, forward-thinking IT companies are also trying to get a foothold in Nagoya. Besides all the attention-gathering news on the redevelopment, Meieki area has good access to Tokyo, Osaka and other major cities. It is also convenient for sales activity and commute. Therefore, it is a popular yet practical business district.
This instructor-led, live training in Nagoya (online or onsite) is aimed at data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms, such as XGBoost, cuML, etc.
By the end of this training, participants will be able to:
Set up the necessary development environment to build data models with NVIDIA RAPIDS.
Understand the features, components, and advantages of RAPIDS.
Leverage GPUs to accelerate end-to-end data and analytics pipelines.
Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
Build data visualizations and execute graph analysis with cuXfilter and cuGraph.
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