• Jeyasri Sekar

The abstract for the research article titled "Deep Learning as a Service (DLaaS) in Cloud Computing: Performance and Scalability Analysis" provides a concise summary of the study, highlighting its purpose, methodology, key findings, and implications. This study investigates the effectiveness and efficiency of delivering deep learning capabilities through cloud computing platforms, focusing specifically on performance and scalability aspects. The primary objectives are to evaluate the speed, accuracy, and resource utilization of various deep learning models when deployed in a cloud environment and to assess the system's ability to scale with increasing workloads. To achieve these objectives, the research employs a comprehensive methodology that includes setting up a DLaaS platform on a cloud infrastructure, selecting representative deep learning models and datasets, and defining appropriate performance and scalability metrics. The experimental setup involves rigorous testing of the chosen models under different conditions to gather data on their execution time, accuracy, response time, throughput, and resource consumption. The main findings of the study reveal significant insights into the performance and scalability of DLaaS. The results demonstrate that while DLaaS platforms can effectively handle complex deep learning tasks, their performance varies depending on the model complexity and the underlying cloud infrastructure. Scalability analysis indicates that DLaaS can manage increasing workloads efficiently up to a certain point, beyond which performance may degrade due to resource constraints. These findings have important implications for both researchers and practitioners in the field of cloud computing and artificial intelligence. They provide a better understanding of the potential and limitations of DLaaS, guiding the development of more robust and efficient cloud-based deep learning services. The study also highlights areas for future research, such as optimizing resource allocation and improving the scalability of DLaaS platforms.

Deep Learning as a Service (DLaaS), Cloud Computing, Performance Analysis, Scalability, Deep Learning

"DEEP LEARNING AS A SERVICE (DLAAS) IN CLOUD COMPUTING: PERFORMANCE AND SCALABILITY ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.i541-i551, March-2023, Available :http://www.jetir.org/papers/JETIR2303879.pdf

"DEEP LEARNING AS A SERVICE (DLAAS) IN CLOUD COMPUTING: PERFORMANCE AND SCALABILITY ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppi541-i551, March-2023, Available at : http://www.jetir.org/papers/JETIR2303879.pdf