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Informative Article | Science and Technology | India | Volume 7 Issue 11, November 2018 | Popularity: 5.1 / 10

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DevOps for Data Science by Bridging the Gap between Development and Data Pipelines

Sumanth Tatineni

Abstract: The evolving technology landscape requires the convergence of DevOps and Data Science, which has become a pivotal force by combining innovation and efficiency to empower organizations with data-driven insights. While traditionally related to software development, DevOps has increased its influence on data science, creating a mutual relationship that bridges the gap between data analysis and development. This article explores the huge significance of implementing DevOps practices in the data science industry, thus addressing challenges and displaying the transformative benefits for organizations aiming to utilize the full potential of their data assets. The collaboration between DevOps and Data Science may initially seem like an unlikely pairing, particularly given their distinct focuses on software development and data analysis. However, this merger holds a huge promise for organizations looking to maximize the value extracted from their data. This paper delves into the DevOps for Data Science concept, depicting how this collaboration accelerates decision-making processes by promoting faster, more reliable and insightful outcomes. The intersection of DevOps and Data Science regarding data-driven decision-making is important for business success. This article explores how DevOps integrates into data science with its core principles of collaboration, automation, and continuous improvement by addressing challenges related to the traditional division between development and data analytics. DevOps practices revolutionize how organizations extract from theory data, mainly reshaping the decision-making approach. The article emphasizes the practical application of DevOps in data science and its role in transforming the reliability and efficiency of overall development. DevOps is slowly gaining recognition as a strong solution for breaking down traditional barriers between operations and developers in contemporary organizations. By emphasizing efficient teamwork and automation, the article highlights how DevOps accelerates delivery speed, promoting overall organizational performance and providing a competitive edge in the market.

Keywords: DevOps, Data Science, integration, machine learning models, automation, data analytics, and continuous integration

Edition: Volume 7 Issue 11, November 2018

Pages: 1960 - 1965

DOI: https://www.doi.org/10.21275/SR231226171114

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