Course details

ML/DL Best Practices: Building Pipelines with Applied Rules

ML/DL Best Practices: Building Pipelines with Applied Rules


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore how to troubleshoot deep learning models and build robust deep learning solutions. Discover the technical challenges of machine learning and how to deal with them and analyze case studies on the impacts of adopting deep learning best practices and deploying deep learning solutions in the enterprise. Review approaches for implementing scalable machine learning systems and rules for building machine learning pipelines into applications. Finally, discover how to use feature engineering to pull features, manage training-serving skew and slowed growth, optimize refinement and complex models.



Expected Duration (hours)
1.1

Lesson Objectives

ML/DL Best Practices: Building Pipelines with Applied Rules

  • discover the key concepts covered in this course
  • list deep learning model troubleshooting steps and recommended data and model checklists for building robust deep learning solutions
  • recognize machine learning technical challenges and the best practices for dealing with the identified challenges
  • use case studies to analyze the impacts of adopting best practices for deep learning
  • identify the challenges and patterns associated with deploying deep learning solutions in the enterprise
  • describe approaches for deploying deep learning solutions in the enterprise using case study scenarios
  • describe approaches for architecting and building machine learning pipelines to implement scalable machine learning systems
  • specify the rules that should be applied while building machine learning pipelines into applications
  • identify the rules that should be applied when using feature engineering to pull the right features into applications
  • specify the causes of training-serving skew and the rules that should be considered to manage training-serving skew
  • define the rules for managing slowed growth, optimization refinement, and complex models in machine learning application management
  • describe checklists for machine learning projects that are to be prepared and adopted by project managers
  • summarize the key concepts covered in this course
  • Course Number:
    it_mlmdbpdj_02_enus

    Expertise Level
    Intermediate