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| S.No | Particular | Page No. | |
|---|---|---|---|
| 1 |
Dr. Rajender KumarAbstract: STUDY ON PYROLYSIS OIL AND ITS UTILIZATION |
|
1-10 |
| 2 |
Dr Lalita KumariAbstract: Climate change is one of the main environmental challenges facing the world today. Several countries facing several problems. Climate change is associated with various adverse impacts on agriculture, water resources, forest and biodiversity, health, coastal management and increase in temperature. Decline in agricultural productivity is the main impact of climate change on India. A majority of population depends on agriculture directly or indirectly. Climate change would represent additional stress on the ecological and socioeconomic systems that are already facing tremendous pressure due to rapid industrialization, urbanization and economic development. This abstract also analyses the impact of climate change and its various aspects in the Indian and world context. |
|
11-17 |
| 3 |
Rohan ShahaneAbstract: The increasing complexity and scale of machine learning (ML) workflows necessitate robust design patterns to ensure efficient data processing, model training, deployment, and scalability. This study explores the design patterns for scalable ML workflows specifically within the Azure ecosystem, including Azure Data Lake and Synapse Analytics. Through a comprehensive literature review, the research identifies key strategies and best practices for optimizing ML workflows, focusing on performance improvement, cost reduction, and scalability. The findings highlight how various design patterns, such as distributed processing, modular pipeline design, and automated model monitoring, contribute to the efficiency and scalability of ML systems. This paper provides a structured framework for organizations to enhance their ML operations, ensuring they can handle growing data volumes and increasing model complexity. The results emphasize the importance of combining computational optimizations with flexible, scalable architectures to future-proof ML systems in cloud environments. |
|
18-28 |
