The Integration of Data Engineering and Cloud Computing in the Age of Machine Learning and Artificial Intelligence

• Author(s): Satyanarayan Kunungo ; Sarath Ramabhotla ; Manoj Bhoyar
• Paper ID: 1700696
• Page: 79-84
• Published Date: 11-05-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 1 Issue 12 June-2018

Abstract

The rapid advancements in machine learning and artificial intelligence have revolutionized the way organizations collect, process, and analyze data. To leverage the full potential of these technologies, there is a growing need for efficient data engineering and scalable computing infrastructure. This abstract explores the integration of data engineering and cloud computing in the age of machine learning and artificial intelligence. Data engineering plays a crucial role in preparing, transforming, and curating data for machine learning models. It involves tasks such as data ingestion, data integration, data quality management, and data pipeline development. Cloud computing, on the other hand, provides on-demand access to computing resources, storage, and services over the internet, offering scalability, flexibility, and cost-effectiveness. This abstract highlights the key benefits of integrating data engineering and cloud computing. Firstly, it enables organizations to handle large volumes of data efficiently, leveraging distributed processing frameworks and parallel computing. Secondly, it facilitates seamless data integration from various sources, including structured and unstructured data, enabling comprehensive analysis and insights. Thirdly, the elastic nature of cloud computing allows organizations to scale their computational resources up or down based on demand, optimizing resource utilization and reducing costs. Furthermore, the abstract discusses various challenges and considerations in the integration process, including data security, privacy, regulatory compliance, and data governance. It emphasizes the importance of robust data management practices, including data encryption, access controls, and data anonymization techniques, to address these concerns. The abstract also highlights the role of cloud-based machine learning platforms and services, which provide pre-built machine learning frameworks, automated model training, and deployment capabilities. This integration empowers organizations to build and deploy machine learning models at scale, accelerating the development and deployment of intelligent applications.

Keywords

Integration, Data engineering, Cloud computing, Machine learning, Artificial intelligence.

Citations

IRE Journals:
Satyanarayan Kunungo , Sarath Ramabhotla , Manoj Bhoyar "The Integration of Data Engineering and Cloud Computing in the Age of Machine Learning and Artificial Intelligence" Iconic Research And Engineering Journals Volume 1 Issue 12 2018 Page 79-84

IEEE:
Satyanarayan Kunungo , Sarath Ramabhotla , Manoj Bhoyar "The Integration of Data Engineering and Cloud Computing in the Age of Machine Learning and Artificial Intelligence" Iconic Research And Engineering Journals, 1(12)