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 training can be carried out locally on customer premises in Uppsala or in NobleProg corporate training centers in Uppsala.
The course location is close to the city center with good access to public transport.
You can discover shopping treasures ...
The course location is close to the city center with good access to public transport.
You can discover shopping treasures at Godsmagasinet, a group of enchanting shops tucked away next to the train station. There is a popular Saturday flea market in Vaksala Square where you can buy souvenirs. You can enjoy a guided candlelit tour of Uppsala Cathedral or admire Viking artefacts, mummies and the eerie 17th-century anatomical theater at the Gustavianum Museum. For a refreshing outdoor afternoon, try a walk along the paths and eat next to the river Fyris, which flows right through the city.
This instructor-led, live training in Uppsala (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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