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LLMs in Focus: From One-Size Fits All to Verticalized Solutions // Venky Ganti & Laurel Orr // #196

MLOps.community

NOTE

Benefits of Task Isolation in Customizing Machine Learning Models

Task isolation in customizing machine learning models provides easier customization to the data, simplifies scoping the problem, and enhances the engineer's ability to turn on the model. Well-scoped tasks make it easier for engineers to optimize model performance and tweak individual components without risking degradation in other tasks. Additionally, task isolation facilitates better training data quality, which has been consistently identified as the most crucial factor in developing high-quality models in machine learning and deep learning.

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