POTENTIALITIES AND CHALLENGES DO GOOGLE COLAB TO MACHINE LEARNING AND BIG DATA ANALYTICS

This research explores the use of Google Colab in the application of Machine Learning and Big Data Analytics techniques, highlighting its advantages and limitations for analyzing large volumes of data. The study contextualizes the growing demand for affordable solutions for implementing Artificial Intelligence and the popularization of cloud-based platforms. The problem addresses the need to investigate the effectiveness of Google Colab as a tool for executing complex analyses in resource-constrained environments. The overall objective was to analyze the use of Google Colab to implement Machine Learning techniques, identifying advantages and limitations. Adopted the paradigm neoperspectivist giftedean, articulating the Theory Symbolic Computing, Constructivist Theory of Knowledge and Deep Learning Theory, and the hypothetical-deductive method for testing hypotheses. The techniques used included descriptive, exploratory and inferential statistical analyses, as well as deep neural networks. The main findings indicate that Colab is efficient in executing AI models, although it presents memory limitations when dealing with large data sets. The theoretical contributions include an expanded understanding of the platform's potential for democratizing AI; methodologically, it offers a replicable framework; and empirically, it demonstrates the viability of Colab in academic contexts. The gaps identified suggest the need for hybrid solutions and integrations with other platforms. The added value lies in offering accessible alternatives for analyzing complex data, enhancing the training of researchers and the application practice in different sectors.