Cloud-Based Machine Learning : Opportunities and Challenges
DOI:
https://doi.org/10.32628/CSEIT24106177Keywords:
Cloud-based Machine Learning, Digital Transformation, Data Privacy & Security, Enterprise Infrastructure, Machine Learning OperationsAbstract
This comprehensive article explores the transformative impact of cloud-based machine learning (ML) on modern enterprises, examining both opportunities and challenges in implementation. The article investigates the rapidly growing cloud computing market and its ML segment, revolutionizing how organizations approach data analytics and business intelligence. Through detailed analysis of enterprise implementations, the article demonstrates how cloud ML solutions have democratized access to advanced analytics, significantly reducing operational costs while improving data processing efficiency. The article examines key aspects, including scalability advantages, cost efficiencies, and technical complexities, while providing evidence-based best practices for successful implementation. Drawing from multiple industry studies and real-world deployments, the article presents a framework for organizations to navigate challenges in data privacy, vendor dependencies, and skill requirements while maximizing the benefits of cloud-based ML solutions.
Downloads
References
Technavio, "Cloud Computing Market Analysis North America, APAC, Europe, South America, Middle East and Africa - US, China, Canada, UK, Germany - Size and Forecast 2024-2028," Technavio Market Research, 2024. [Online]. Available: https://www.technavio.com/report/cloud-computing-market-size-industry-analysis
Nandini Sharma, "A Review on Democratization of Machine Learning In Cloud," Journal of emerging technologies and innovative research, 2020. [Online]. Available: https://www.academia.edu/103196314/A_Review_on_Democratization_of_Machine_Learning_In_Cloud
Gbor G Dooshima, Emmanuel Ogala, and N V Blammah, "Scalable Architecture for Cloud-Based Machine Learning and Data Analysis," October 2024. [Online]. Available: https://www.researchgate.net/publication/384798604_Scalable_Architecture_for_Cloud-Based_Machine_Learning_and_Data_Analysis
Google Cloud Platform, "AI and ML perspective: Performance optimization," Google Cloud Architecture Framework, 2024. [Online]. Available: https://cloud.google.com/architecture/framework/perspectives/ai-ml/performance-optimization
Younis A Younis, Kashif Kifayat, and Madjid Merabti, "Cloud Computing Security & Privacy Challenges," The 15th Annual PostGraduate Symposium on The Convergence of Telecommunications, Networking and BroadcastingAt: Liverpool, June 2014. [Online]. Available: https://www.researchgate.net/publication/268445145_Cloud_Computing_Security_Privacy_Challenges
Marigo Raftopoulos, and Juho Hamari, "Organizational Challenges in Adoption and Implementation of Artificial Intelligence," Hawaii International Conference on System Sciences (HICSS) At: Honolulu, Hawaii, USA, April 2024. [Online]. Available: https://www.researchgate.net/publication/379831873_Organizational_Challenges_in_Adoption_and_Implementation_of_Artificial_Intelligence DOI: https://doi.org/10.24251/HICSS.2023.698
Amazon Web Services, "Enterprise Guide for Machine Learning Implementation," AWS Technical Publication, 2024. [Online]. Available: https://pages.awscloud.com/rs/112-TZM-766/images/AWS_ML_Enterprise_Guide_final.pdf
Cloud Security Alliance, "Mitigating Risk in Cloud Computing: A Comprehensive Analysis," Cloud Security Technical Report, 2024. [Online]. Available: https://edtechmagazine.com/k12/sites/edtechmagazine.com.k12/files/document_files/151383-Mitigating-Risk-in-the-Cloud.pdf
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
