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. Maastricht onsite live RAPIDS trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
The meeting rooms at Stationsplein in Maastricht are located in an inspiring high building with large windows to enjoy the vi...
The meeting rooms at Stationsplein in Maastricht are located in an inspiring high building with large windows to enjoy the view over this international city. Outside spraying fountains that provide a soothing environment to work. A number of national and international service companies have established themselves in this part of the Netherlands, and in the city there are also many large companies, including specialists in logistics, IT, medical and veterinary technologies and life sciences. There are also a number of large call centers here. Many important Dutch and European government offices are located in the city.
This instructor-led, live training in Maastricht (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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