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 Leipzig can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Leipzig
NobleProg Leipzig, Arthur Hoffmann Str., Leipzig, germany, 04107
Overview
Our training facilities are located at Arthur Hoffmann Str. in Leipzig. Our spacious c...
Overview
Our training facilities are located at Arthur Hoffmann Str. in Leipzig. Our spacious classrooms are centrally located near the university and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the Bayerischer Bahnhof S-Bahn station. In the north you reach the motorway A41, in the south the motorway A38.
Parking
You will find numerous parking spaces around our training rooms.
Local Services
You will find numerous parking spaces around our training rooms.
This instructor-led, live training in Leipzig (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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