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. Onsite live RAPIDS training can be carried out locally on customer premises in Cologne or in NobleProg corporate training centers in Cologne.
Our training facilities are located at Theodor-Heuss-Ring 23 in Cologne. Located in a ...
Overview
Our training facilities are located at Theodor-Heuss-Ring 23 in Cologne. Located in a modern office building, our premises offer enough space for successful training courses centrally in Cologne, within walking distance of the underground station Ebertplatz.
Arrival
The NobleProg training facilities are located on the left bank of the Rhine, easily accessible from Cologne Central Station via underground lines 16 and 18. The building has a limited number of parking spaces.
Parking
Cars can be parked in the area along the Torstrasse and the nearby side streets, even if you may have to search for a moment. There is no charge for parking.
Local Services
In the area of Cologne's old town there are numerous localities and around the Cologne Cathedral, the landmark of the city, there are numerous hotels.
This instructor-led, live training in Cologne (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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