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<journal-id journal-id-type="publisher">london-journal-of-research-in-computer-science-technology</journal-id>
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<journal-title>London Journal of Research in Computer Science &amp; Technology</journal-title>
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<issn publication-format="print">2514-863X</issn>
<issn publication-format="electronic">2514-8648</issn>
<publisher><publisher-name>JournalsPress</publisher-name></publisher>
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<article-id pub-id-type="publisher-id">91347</article-id>
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<article-title>Data Science in the Cloud Overcoming Challenges and Maximizing Opportunities for Machine Learning</article-title>
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<volume>23</volume>
<issue>2</issue>
<fpage>5</fpage>
<lpage>6</lpage>
<abstract><p>As Data Science is a rapidly evolving field in which large amounts of data are analyzed to derive meaningful insights using machine learning-based techniques. In contrast, cloud computing offers a scalable and cost-effective platform for storing and processing data for machine learning-based applications. The combination of data science and cloud computing has emerged as a powerful tool for organizations seeking a competitive advantage through data- driven decision-making. The problem statement is that traditional machine learning approaches frequently involved storing data on local servers and analyzing it with specialized tools. However, this approach can be costly, time-consuming, and unscalable in the face of large data volumes. Many organizations are turning to cloud-based machine-learning platforms to address these challenges.</p></abstract>
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<title>Full Text</title>
<p>As Data Science is a rapidly evolving field in which large amounts of data are analyzed to derive meaningful insights using machine learning-based techniques. In contrast, cloud computing offers a scalable and cost-effective platform for storing and processing data for machine
learning-based applications. The combination of data science and cloud computing has emerged as a powerful tool for organizations seeking a competitive advantage through data- driven decision-making. The problem statement is that traditional machine learning approaches
frequently involved storing data on local servers and analyzing it with specialized tools. However, this approach can be costly, time-consuming, and unscalable in the face of large data volumes. Many organizations are turning to cloud-based machine-learning platforms to address
these challenges.</p>
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