In recent years, there has been a significant surge in the adoption of artificial intelligence (AI) and machine learning (ML) technologies across a wide range of industries. Frameworks such as TensorFlow, PyTorch, and Scikit-learn have emerged as popular choices for AI development due to their versatility and robustness. However, the seamless integration of AI into enterprise-grade, production-ready applications poses unique challenges that need to be addressed.
Spring, a widely recognized enterprise-level framework, is celebrated for its exceptional robustness, scalability, and flexibility in crafting sophisticated applications. Nevertheless, the question arises: can Spring effectively cater to the intricate demands of AI/ML-based applications? This article aims to explore the depths of Spring’s capabilities within the AI domain, its potential integration with AI libraries, and its ability to effectively manage AI workflows within production environments.
