Machine Learning Pipelines for Automated Big Data Analysis
DOI:
https://doi.org/10.15680/IJCTECE.2021.0404001Keywords:
Machine Learning, Big Data, Automation, ML Pipelines, Data Preprocessing, Feature Extraction, Model Training, Cloud Computing, Scalability, Predictive Analytics, Data PrivacyAbstract
The rise of big data has created an urgent need for efficient and scalable data processing techniques. Traditional data analysis methods struggle to keep pace with the volume, variety, and velocity of big data. Machine Learning (ML) pipelines provide a robust solution for automating the process of data analysis, enabling organizations to extract valuable insights efficiently from massive datasets. These pipelines integrate various stages of machine learning, such as data preprocessing, feature extraction, model training, evaluation, and deployment, into a seamless and automated workflow. This paper explores the role of ML pipelines in automated big data analysis, discussing the components, design, and implementation of these pipelines. We examine how cloud computing platforms, such as AWS, Google Cloud, and Microsoft Azure, facilitate the construction of scalable ML pipelines. Furthermore, we highlight various use cases of ML pipelines across industries, including healthcare, finance, and e-commerce. Challenges such as handling unstructured data, ensuring model interpretability, and managing the scalability of ML pipelines are also addressed. The study concludes by discussing the potential benefits of implementing ML pipelines, including improved decision-making, increased efficiency, and the democratization of machine learning for non-expert users. Additionally, the importance of maintaining ethical considerations and data privacy within these automated workflows is emphasized.
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