Effective Machine Learning with Dask

PT26961
Summary
This class focuses on leveraging Dask for Machine Learning in several different ways: Dask implements a number of distributed algorithms; interoperates with popular Python libraries, and integrates with several external projects (e.g., PyTorch). This module looks at each of the options, as well as the full ML lifecycle, from ingesting data to performing inference.
Prerequisites
The following prerequisites are required for this course:
  • Python, basic level
  • Understanding of ML concepts and workflow, basic level
  • Dask programming, basic level
Duration
1 Day/Lecture & Lab
Audience
This course is intended for those who manage machine learning workflows.
Topics
  • Introduction
  • Dask and scikit-learn
  • Data Preparation and Dask’s Algorithms and Integration with XGBoost
  • Performing Inference at Scale
  • Custom Algorithms
  • Review Q & A

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