The Machine Learning Life Cycle Explained 2024

The Machine Learning Lifecycle involves multiple stages: data collection, preprocessing, model selection, training, evaluation, and deployment. This structured approach ensures efficient resource allocation and problem-solving, promoting continuous improvement and collaboration. Iterative refinement enhances model accuracy and efficiency. The lifecycle's methodical design supports effective project management, reducing risks and improving the success rate of machine learning projects.

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