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 Salzburg can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located atAdolf-Kolping-Str. 10 in Salzburg. Our spacious ...
Overview
Our training facilities are located atAdolf-Kolping-Str. 10 in Salzburg. Our spacious training rooms are located near the Salzach river, within walking distance of the old town, and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the A1 motorway and the train station and Europark Salzburg Taxham train station are easily accessible.
Parking
Parking is available in the surrounding streets around our training rooms.
Local Services
In the area of the city centre there are numerous restaurants and also hotels are within walking distance.
This instructor-led, live training in Salzburg (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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