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. Yokohama onsite live RAPIDS trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
The centre is located within a 5-minute walk from JR Kannai station and a 3-minute walk from Yokohama Blue Line Subway’s Ka...
The centre is located within a 5-minute walk from JR Kannai station and a 3-minute walk from Yokohama Blue Line Subway’s Kannai station. On the north side of the centre is the traditional business district lined with governmental offices, including the prefectural office, Yokohama’s city hall, customs , and port authorities. The area is home to a large number of import / export companies. The city is known for its international air which attracts expats as well as the good old cultural heritage loved by tourists. Tourism thrives here, with many attractions, hotels near the waterfront and the retro downtown area of Motomachi. Yokohama station, one of the biggest hubs in the Tokyo metropolitan area is less than a 10 minute train-ride from this centre. Yokohama station connects to many railway companies and you will reach Tokyo or Shibuya station in 30 minutes.
This instructor-led, live training in Yokohama (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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