Feature engineering

Các khái niệm về MLOps

Folkert Stijnman

ML Engineer

Feature engineering

MLOps phases feature engineering

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Feature engineering

... is the process of selecting, manipulating, and transforming raw data into features.

  • A feature is a variable, such as the column in a table
  • We can use raw data, but also create our own
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Customer data

Example customer data

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Customer data

Example customer data with new feature

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Feature engineering

  • Goal is to enhance model performance
  • Tools and techniques help to process, select, and maintain features:
    • Feature selection
    • Feature store
    • Data version control
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Feature selection

  • Domain-specific knowledge
  • Correlation
  • Feature importances
  • Other methods: univariate selection, Principal Component Analysis (PCA), Recursive Feature Elimination (RFE)

correlation plot

1 https://www.datacamp.com/tutorial/tutorial-datails-on-correlation
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The feature store

Feature store

Only relevant for large teams working on multiple projects that use the same features

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Data version control

  • Tracking dataset changes
  • Maintaining consistency throughout the development lifecycle

graphic depicting data version control and git

1 https://www.datacamp.com/courses/cicd-for-machine-learning
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Let's practice!

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