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. Stuttgart onsite live RAPIDS trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located at Löffelstraße 40 in Stuttgart. Our spacious tr...
Overview
Our training facilities are located at Löffelstraße 40 in Stuttgart. Our spacious training rooms are easy to reach thanks to good traffic connections and offer optimal training conditions.
Arrival
The NobleProg training facilities are conveniently located near the B27, the feeder road to the A8 motorway. If you arrive by public transport, the nearest underground station is Degerloch/Albstrasse.
Parking
Around our training rooms you will find numerous parking spaces which are not subject to charges.
Local Services
Not far from Löfflerstrasse you will find various restaurants and hotels, including the Hotel Harambe Afrika.
This instructor-led, live training in Stuttgart (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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