Machine Learning Engineers (MLEs) rely on Python decorators to streamline their machine learning workflows. This article introduces 10 essential decorators with practical code examples. The decorators covered include Memoization, Timing, Validation, Retry, Logging, Parameter Validation, Data Preprocessing, Model Persistance, Performance Profiling, and Experiment Tracking. These decorators enhance efficiency, provide insights, and streamline code, making the journey in machine learning more productive and rewarding.

source update: 10 Decorators I Use Daily as a Tech MLE – Towards AI


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