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 Bern can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
NobleProg Bern
Seidenweg 3, Bern, Switzerland, 3001
Overview
Our training facilities are located at Seidenweg 3 in Bern. Our spacious classrooms are ...
Overview
Our training facilities are located at Seidenweg 3 in Bern. Our spacious classrooms are located very centrally, not far from the university, and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are easily accessible via the A1 motorway. If you arrive by public transport, you can reach the training rooms via the Mittelstrasse stop.
Parking
Rund um unsere Schulungsräume gibt es in den umliegenden Straßen Parkmöglichkeiten.
Local Services
There are numerous restaurants and hotels within walking distance of the old town, the so-called Red Quarter.
This instructor-led, live training in Bern (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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