In contemporary industrial production, the trend toward automation is pervasive across all sectors, highlighting the necessity for effective tools to facilitate the development of automated systems. This article explores the implementation of the AutoML approach to streamline the creation of machine learning models, particularly for applications in automatic monitoring. The AutoML approach offers significant advantages in the field of machine learning by automating the process of model selection, tuning, and evaluation. By reducing the reliance on extensive domain expertise, AutoML democratizes access to advanced analytical tools, enabling non-experts to effectively develop models. This approach enhances efficiency, as it streamlines workflows and minimizes the time required to deploy models in practical applications. The paper proposes the developed software plat-form AutoGenNet. It is implemented within the AutoML paradigm and utilises the No-Code development concept. This concept effectively abstracts the complexities associated with model creation and training, thereby lowering the entry barrier for users without extensive technical knowledge. By utilising No-Code methodologies, the platform not only simplifies user interaction but also increases accessibility. Furthermore, the AutoGenNet platform includes a mechanism for automatically generating software wrappers, facilitating efficient operation of trained models. This comprehensive integration allows for the effective application of the AutoML approach in automating the generation and training processes of the machine learning model. As a result, the system significantly accelerates and simplifies the resolution of automatic monitoring tasks utilizing machine learning methodologies. In addition, the developed system is designed with scalability in mind, allowing it to be adapted in the future for automated generation of various models of other machine learning architectures. This flexibility opens up new possibilities for solving a variety of practical problems in monitoring applications, thus extending the usefulness of machine learning in industrial contexts.