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Generative AI in AutoML

This report explores the potential of generative AI techniques in optimizing various processes within AutoML and the steps that organizations need to consider in order to optimally leverage Generative AI in AutoML.

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Prior to the advent of generative AI, AutoML (Automated Machine Learning) operated primarily by automating the process of feature engineering, model selection and hyperparameter tuning, with the goal of optimizing the performance of machine learning models and making it more accessible to users without extensive expertise in data science.

However, the emergence of generative AI has transformed the landscape, by offering new opportunities to enhance data augmentation and automate feature generation, enabling AutoML systems to explore a wider range of model architectures, and discover novel patterns and relationships in the data. All these enhance the system’s ability to adapt to complex datasets and improve model performance and efficiency.

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Integrating generative AI into AutoML is becoming a promising trend in the market. This report explores the potential of generative AI techniques in optimizing various processes within AutoML and the steps that organizations need to consider in order to optimally leverage Generative AI in AutoML.

 

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