Browsing by Author "Poojary, K.K."

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    A Comprehensive Review on Scaling Machine Learning Workflows Using Cloud Technologies and DevOps
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Vaikunta Pai, T.; Birǎu, R.; Poojary, K.K.; Abhay; Shingad, A.R.; Sowjanya, N.; Popescu, V.; Mitroi, A.T.; Nioata, R.M.; Kiran Raj, K.M.
    Scaling Machine Learning (ML) workflows in cloud environments presents critical challenges in ensuring reproducibility, low-latency inference, infrastructure reliability, and regulatory compliance. This review addresses the lack of a comprehensive synthesis of how integrated DevOps practices and cloud-native technologies enable scalable, production-grade ML systems. We analyze the convergence of MLOps with tools such as Kubernetes, Jenkins, and Terraform, detailing their role in automating CI/CD pipelines, infrastructure provisioning, and model lifecycle management. The main highlights strategies for optimizing resource utilization, minimizing inference latency, and managing data versioning across hybrid and multi-cloud architectures (AWS, Azure, GCP). We also examine serverless computing, container orchestration, and monitoring practices to enhance scalability and governance. By categorizing challenges chronologically and evaluating emerging practices such as federated learning and security-by-design, this work bridges a key gap in existing literature. It offers a unified perspective on building reliable, reproducible, and compliant ML workflows, thereby advancing the state of scalable AI system engineering. © IEEE. 2013 IEEE.
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    Inorganic Chemical Reaction Predictor Using Random Forest and Support Vector Machine
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Sahil, M.; Palan, S.A.; Bhandary, D.; Shetty, S.S.; Poojary, K.K.; Sowjanya, N.
    The Chemical Reaction Predictor project shall use machine learning approaches to make predictions on chemical reaction effects. When a large enough group of known reactions is available, each identified set of reactants and products can be used to construct a model into which can be fed any set of reactants. It includes data acquisition and data pre-processing, feature selection of reactant properties and reaction conditions, and construction of several predictive models. The first and main goal is to dogmatically apply machine learning models such as Random Forests and Support Vector Machines to attain an accuracy of 60% or higher. Furthermore, we measure the accuracy, and other measures such as precision, recall, and F1 score to determine the efficiency of these models. Finally, while the optimal model is found and implemented, it is brought within a simple graphical user interface that enables the users to input reactants and obtain predicted products. © 2025 IEEE.