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Exploring the Future of AI Workflows
The chapter delves into the evolution of machine learning, comparing traditional ML with language model science, and forecasting potential disruptions in legacy software. It discusses the future of ML models in production, predicting the ongoing prevalence of random forests and cautioning against the overuse of models without proper evaluation. The conversation also highlights the significance of understanding data and utilizing diverse tools like deep learning for accuracy in AI applications.