Artificial intelligence has become indispensable in the field of data technologies. It allows us to build and use highly accurate predictive models that help us make important decisions.
The aim of this thesis is to present the use of automated machine learning in modern database management systems, with a case study of MindsDB. The thesis delves into the challenges of machine learning, demonstrates the use of large language models, and applies predictive models to diverse data sources using the CRISP-DM methodology. The results are quantitatively compared with traditional machine learning techniques. MindsDB has demonstrated success and versatility across various predictive modeling tasks, as well as the ability to integrate large language models, highlighting its usefulness in different scenarios.
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