Integrating Machine Learning with Cloud Computing for Intelligent and Resilient Systems with Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806032Keywords:
machine learning, cloud computing, intelligent systems, resilient systems, cloud analytics, predictive analytics, distributed systems, fault tolerance, anomaly detection, real-time processingAbstract
The integration of machine learning (ML) with cloud computing has emerged as a transformative approach for developing intelligent and resilient systems capable of advanced analytics. Cloud platforms provide scalable infrastructure and computational resources, while ML algorithms enable systems to learn from data, adapt to changing conditions, and make informed decisions. This synergy supports a wide range of applications, including predictive analytics, anomaly detection, and automated system optimization. However, the increasing reliance on cloud-based ML systems introduces challenges related to data security, system reliability, and performance under dynamic workloads. This study explores the architectural design, implementation strategies, and best practices for integrating ML with cloud computing to build intelligent and resilient systems. It examines key components such as distributed data processing, real-time analytics pipelines, and fault-tolerant architectures. The research also highlights the role of resilience mechanisms, including redundancy, load balancing, and self-healing systems, in ensuring continuous operation. By leveraging ML-driven analytics within cloud environments, organizations can enhance system performance, improve decision-making, and ensure robustness against failures and uncertainties. The study concludes that effective integration of ML and cloud technologies is essential for building next-generation intelligent systems.
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