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Model-Centric ML

What is model-centric ML?

Model-centric ML is an approach to machine learning that focuses on iteratively improving model architecture and hyperparameters to enhance model performance. This method emphasizes the importance of finding the best model architecture, configuration, and hyperparameters to achieve the desired level of accuracy and generalization for a given task.

When should you consider model-centric ML?

You need to consider model-centric ML when the primary goal of your machine learning project is to optimize and improve the model architecture and performance of your models for a static training dataset. This approach is particularly relevant when dealing with complex problems, where the choice of architecture and hyperparameters can have a significant impact on the model's accuracy and efficiency.

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