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 trainings in Bremen can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located at Konsul-Smidt-Str. 8 in Bremen. Our spacious tra...
Overview
Our training facilities are located at Konsul-Smidt-Str. 8 in Bremen. Our spacious training rooms are located near the Weser river, within walking distance of the European port, and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the Konsul-Smidt-Strasse stop, Bremen main station is easy to reach and Bremen can best be reached by car via the A1 motorway.
Parking
Parking is available in the surrounding streets around our training rooms.
Local Services
In the area of the Weser bank there are numerous restaurants and the city centre is within walking distance.
This instructor-led, live training in Bremen (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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