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 Munich can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Munich
NobleProg München , Landshuter Allee 8-10, München, germany, 80637
Overview
Our training facilities are located at Landshuter Allee 8-10 in Munich. Thanks t...
Overview
Our training facilities are located at Landshuter Allee 8-10 in Munich. Thanks to good transport connections, our spacious training rooms are easy to reach and offer optimal training conditions.
Directions
The NobleProg training facilities are conveniently located not far from the A95 and A96 motorways. The underground station Rotkreuzplatz and the S-Bahn station Donnersbergerbrücke are within walking distance.
Parking spaces
There are numerous free parking spaces around our training rooms.
Local infrastructure
Not far from Landshuter Allee you will find various restaurants and in the nearby Arnulfstrasse there are several hotels.
This instructor-led, live training in Munich (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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