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 Dehiwala- Mount Lavinia can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Dehiwala
No 154, Hotel Road, Dehiwala- Mount Lavinia, sri lanka, 10370
The Centre located in the coastal town of Mount Lavinia, which is just 12 kilometres south of central Colombo and 54 kilometr...
The Centre located in the coastal town of Mount Lavinia, which is just 12 kilometres south of central Colombo and 54 kilometres from the Bandaranaike International Airport and 4-minute walk from the Mount Lavinia railway station, and 4 km from the National Zoological Gardens of Sri Lanka. Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
This instructor-led, live training in Dehiwala- Mount Lavinia (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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