Transparent Peer Review By Scholar9
Integrating Artificial Intelligence and Machine Learning into Cloud Platforms: Analyzing Impacts on Resource Management, Scalability, and Application Performance
Abstract
The combination of artificial intelligence (AI) and machine learning (ML) in cloud platforms has transformed the landscape of cloud computing, AI and ML in leading cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and in Google Cloud Platform (GCP) . The current state of the application AI and ML applications, such as automatic machine learning (AutoML), natural language processing (NLP), and predictive analytics are reviewed in this workshop to empower them advance and provide users with advanced tools for data analysis and decision making. AI-powered cloud resource management tools optimize resource allocation and costing using machine learning algorithms. These tools enable dynamic measurement, predictive maintenance, and intelligent load balancing by analyzing historical data and real-time application schedules. This improves efficiency and reduces operating costs. Scalability has increased exponentially as AI algorithms can predict demand increases and adjust resources accordingly, ensuring applications remain viable and reliable under different loads. AI and ML have a significant effect on the performance of applications. Personalized recommendations from machine learning models can improve user experiences, streamline application workflows, and automate laborious chores. Furthermore, better insights into application performance are provided by AI-enhanced monitoring tools, which speed up problem solving and promote proactive system management. Potential difficulties are also covered in this article, including data privacy issues and the difficulty of integrating AI and ML into current cloud infrastructures. The results highlight how AI and ML may revolutionize cloud settings by providing a competitive advantage through better resource management, scalable solutions, and improved application performance.
Phanindra Kumar Kankanampati Reviewer
03 Oct 2024 11:59 AM
Not Approved
Relevance and Originality
The text effectively addresses the transformative impact of AI and ML on cloud computing platforms, which is highly relevant given the rapid evolution of technology in this field. By focusing on leading cloud providers like AWS, Azure, and GCP, the study provides original insights into how these technologies enhance data analysis and decision-making tools. The exploration of specific applications, such as AutoML and predictive analytics, adds depth to the discussion, demonstrating the practical implications of AI in cloud environments.
Methodology
While the text outlines the applications of AI and ML in cloud platforms, it lacks specific methodological details regarding the review or workshop context mentioned. Including information about how the review was conducted—such as criteria for selecting applications, data sources, and evaluation metrics—would strengthen the methodology. A more detailed description of the analytical processes used to assess the effectiveness of AI-powered tools would enhance the study’s rigor.
Validity & Reliability
The claims about the benefits of AI and ML in cloud platforms are compelling but would be bolstered by empirical data or case studies illustrating these advantages. Including specific examples of performance improvements or efficiency gains achieved through AI and ML applications would enhance the reliability of the findings. Discussing potential biases or limitations in the data or methodologies employed would also provide a more balanced view.
Clarity and Structure
The text is generally clear but could benefit from improved organization. Dividing the content into distinct sections—such as "Introduction," "AI and ML Applications in Cloud Computing," "Benefits of AI-Powered Tools," "Challenges and Limitations," and "Conclusion"—would improve readability and logical flow. Clearly defining key terms like "AutoML," "NLP," and "predictive analytics" would make the content more accessible to readers with varying levels of expertise.
Result Analysis
The analysis of the impact of AI and ML on cloud operations is insightful but could be enhanced by including specific quantitative results or metrics that demonstrate improvements in efficiency and performance. Discussing the implications of these findings for businesses and outlining best practices for implementing AI in cloud environments would provide additional value. Furthermore, addressing future trends in AI and ML, such as the potential for further automation and enhanced predictive capabilities, could enrich the discussion and highlight ongoing opportunities for innovation in cloud computing.
IJ Publication Publisher
Ok Sir
Phanindra Kumar Kankanampati Reviewer