Decoupling
Decoupling refers to the process of separating the components of a system in order to reduce dependencies and increase modularity. In the context of data science and artificial intelligence, decoupling can be used to improve the efficiency and scalability of machine learning models. By breaking down a complex model into smaller, independent components, each component can be optimized and updated without affecting the rest of the system. This allows for faster experimentation and iteration, as well as easier maintenance and troubleshooting. Decoupling can also improve the interpretability and explainability of models, as each component can be analyzed and understood separately. Overall, decoupling is a powerful technique for building robust and flexible machine learning systems.
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