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ML/DL Best Practices: Machine Learning Workflow Best Practices

ML/DL Best Practices: Machine Learning Workflow Best Practices


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore the phases of machine learning and deep learning workflows, as well as the data workflows that can be used to develop machine learning models, the best practices for building robust machine learning systems, and the challenges associated with debugging models. Discover the benefits of using checklists to develop and implement end-to-end machine learning and deep learning workflows and models, how to identify and fix issues associated with training, generalizing and optimizing machine learning models, and common design choices for implementing deep learning projects.



Expected Duration (hours)
0.9

Lesson Objectives

ML/DL Best Practices: Machine Learning Workflow Best Practices

  • discover the key concepts covered in this course
  • list the various phases of machine learning workflow that can be used to achieve key milestones of machine learning projects
  • recall the data workflows that are used to develop machine learning models
  • identify the differences between machine learning and deep learning and illustrate the phases of deep learning workflow
  • list the best practices that should be adopted to build robust machine learning systems, with focus on the evaluation approach
  • recall the challenges of debugging machine learning and deep learning projects and the factors that need to be considered while debugging
  • describe the approach of debugging trained machine learning models using flippoints
  • recognize the benefits of implementing machine learning checklists and the process of building checklists that can be used to work through applied machine learning problems
  • describe checklists for debugging neural networks, the steps involved in identifying and fixing issues associated with training, and generalizing and optimizing machine learning models
  • recall the checklists for implementing end-to-end machine learning and deep learning workflows that should be adopted to build optimized machine learning and deep learning models
  • describe the high-level deep learning strategies and the common design choices for implementing deep learning projects
  • summarize the key concepts covered in this course
  • Course Number:
    it_mlmdbpdj_01_enus

    Expertise Level
    Intermediate