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 Dresden or in NobleProg corporate training centers in Dresden.
Our training facilities are located at Weißeritzstrasse 30 in Dresden. Our spacious t...
Overview
Our training facilities are located at Weißeritzstrasse 30 in Dresden. Our spacious training rooms are located directly west of Dresden's old town and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the tram stop Kongresszentrum and the A17 motorway is also easily accessible.
Parking
The Mitte multi-storey car park, right around the corner, offers guarded parking spaces.
Local Services
Within walking distance is the Leonardo Hotel Dresden Altstadt, other hotels and a variety of restaurants are also nearby.
This instructor-led, live training in Dresden (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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