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AI Engineer, Web Developeredit pen
Article
Real World Use Cases: Strategies that Will Bridge the Gap Between Development and Productionizing
A comprehensive guide on transitioning machine learning models from development to production, focusing on optimizing metrics, handling uncertainty, and ensuring model explainability. The article uses
a fraud detection case study to demonstrate practical implementation of these concepts using tools like TabNet and Optuna.

Reasons to Read -- Learn:

  • how to properly align your machine learning metrics with business objectives, demonstrated through a practical example of adjusting F-beta scores and BCE loss weights in fraud detection
  • strategies for presenting honest model performance metrics to stakeholders, including proper data splitting techniques and validation approaches that prevent inflated results
  • practical approaches to handling model uncertainty and data drift in production systems, with specific techniques for both classification and regression problems
    • 8 min readauthor: Hampus Gustavsson
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