Enhancing Data Science Outcomes With Efficient Workflow (EDSOEW)

 

Resumen del Curso

Learn how to create an end-to-end, hardware-accelerated machine learning pipeline for large datasets. Throughout the development process, you’ll use diagnostic tools to identify delays and learn to mitigate common pitfalls.

Prerrequisitos

  • Basic knowledge of a standard data science workflow on tabular data. To gain an adequate understanding, we recommend this article.
  • Knowledge of distributed computing using Dask. To gain an adequate understanding, we recommend the “Get Started” guide from Dask.
  • Completion of the DLI’s Fundamentals of Accelerated Data Science course or an ability to manipulate data using cuDF and some experience building machine learning models using cuML.

Objetivos del curso

  • Develop and deploy an accelerated end-to-end data processing pipeline for large datasets
  • Scale data science workflows using distributed computing
  • Perform DataFrame transformations that take advantage of hardware acceleration and avoid hidden slowdowns
  • Enhance machine learning solutions through feature engineering and rapid experimentation
  • Improve data processing pipeline performance by optimizing memory management and hardware utilization

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Precios & Delivery methods

Entrenamiento en línea

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0,5 días

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Classroom training

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Instructor-led Online Training:   Este es un curso en línea Guiado por un Instructor. If you have any questions about our online courses, feel free to contact us via phone or Email anytime.

Estados Unidos de América

Entrenamiento en línea 07:30 Pacific Daylight Time (PDT) Este curso será presentado por un socio Inscripción