Domain Adaptation

ML models transform operations in almost all sectors, ranging from medical diagnosis to production lines. But how can we increase the robustness of these models to address data drift between training data and production data?

The answer lies in Domain adaptation, the application of trained algorithms from one or more source domains to a target domain with the same feature or characteristics but different data distributions.

This whitepaper discusses how to use Domain Adaptation to build robust Machine Learning models that are less susceptible to data drift and perform well in production settings, even if data distribution changes