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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Event-Driven Machine Learning Integration in Cloud Ecosystems: Leveraging AWS SageMaker and Lambda for Scalable, Real-Time Intelligence |
| Authors Name: | Divyesh Pradeep Shah |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206056
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| Published Paper Id: | IJRTI2509028 |
| Published In: | Volume 10 Issue 9, September-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i9.206056 |
| Abstract: | 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. |
| Keywords: | Machine Learning Integration, Cloud Ecosystems, AWS SageMaker, AWS Lambda, Serverless Architecture, Real-Time Inference, Model Retraining, Solar Forecasting, Event-Driven Systems, Scalable ML Workflows |
| Cite Article: | "Event-Driven Machine Learning Integration in Cloud Ecosystems: Leveraging AWS SageMaker and Lambda for Scalable, Real-Time Intelligence", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a225-a231, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509028.pdf |
| Downloads: | 0002047 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2509028
Registration ID:206056
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i9.206056
Page No: a225-a231 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509028 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509028 |
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ISSN: 2456-3315
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Journal Starting Year (ESTD) : 2016
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