Choosing the Right Tech Stack for Developing Artificial Intelligence Applications

Abstract

 This article is dedicated to the study of methods for selecting a technology stack for developing artificial 
intelligence (AI)-based applications. Through a systematic review of domestic and international literature, key factors 
influencing the choice of tools and platforms are examined, including requirements for performance, scalability, and 
integration with existing systems. Popular libraries and frameworks such as TensorFlow, PyTorch, and Scikit-learn, 
as well as cloud services like AWS AI, Google AI, and Microsoft Azure AI, are analyzed. The article reviews the 
features, advantages, and application areas of different programming languages, including Python, Java, and C++. 
Special attention is given to the selection of databases and infrastructure suitable for handling Big Data and deploying 
machine learning models. Best practices in DevOps and MLOps, which ensure automation of AI model development, 
testing, and deployment, are also discussed. Based on the analysis, recommendations are provided for choosing a 
technology stack depending on the specifics of the project, including tasks related to computer vision, natural language 
processing (NLP), and predictive analytics. The presented findings may be useful for developers, data engineers, and 
project managers involved in AI implementation across various fields such as finance, healthcare, education, and 
manufacturing.