Machine Learning and Artificial Intelligence in Cloud Computing

Abstract

The field of technology is constantly evolving, and at the forefront of this progress lies the powerful synergy between cloud computing and machine learning (ML). Cloud computing provides a robust and scalable platform that serves as the launchpad for advancements in artificial intelligence (AI), particularly machine learning. This platform offers features that empower ML development, including access to vast and scalable resources, cost-effective solutions, collaborative tools, and global reach. Machine learning, in turn, becomes the engine that propels cloud applications forward, enabling them with functionalities like predictive analytics, personalized experiences, automated operations, and anomaly detection. This paper delves into the intricate details of how cloud computing empowers ML and vice versa. It explores real-world examples that showcase the practical application of this powerful partnership, while acknowledging the challenges that come with integrating these two transformative technologies. By addressing security, privacy, model bias, and explainability concerns, this convergence has the potential to shape a future that is not only more intelligent but also deeply data-driven. Keywords: Cloud computing, machine learning, artificial intelligence (AI), ML, scalability, cost- effectiveness, collaboration, global availability, predictive analytics, personalization, automation, anomaly detection, cloud AI platforms (e.g., Azure ML, SageMaker, Google AI Platform), security, privacy, model bias, explainability.