Event Driven Machine Learning Integration in Cloud Ecosystems: Leveraging AWS SageMaker and Lambda for Scalable, Real Time Intelligence

The growing complexity and volume of data across sectors have accelerated the adoption of machine learning (ML) within cloud-native environments.This review explores the integration of ML workflows using AWS SageMaker and AWS Lambda, presenting a comprehensive examination of recent developments, key research findings, and evolving challenges.Through a detailed review of the literature, we identify critical gaps in current ML deployment models-particularly in terms of scalability, retraining automation, and resource optimization.In response, we introduce the Event-Driven Adaptive Machine Learning Framework (EDAMLF), a novel model that leverages serverless computing for dynamic retraining and low-latency inference.We benchmark EDAMLF against existing architectures, demonstrating superior performance in predictive accuracy and cost-efficiency.The review also discusses case studies involving solar forecasting, illustrating real-world applicability and societal relevance.Finally, we provide policy and practical implications, highlighting future research opportunities at the intersection of event-driven computing and machine learning.