Building Scalable Data Pipelines for Machine Learning: Architecture, Tools, and Best Practices

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@article{1707513,
      author = {Bhanu Prakash Reddy Rella},
      title = {Building Scalable Data Pipelines for Machine Learning: Architecture, Tools, and Best Practices},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {7},
      pages = {511-527},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707513.pdf},
      abstract = {Building scalable data pipelines is crucial for efficient machine learning (ML) workflows, ensuring seamless data ingestion, transformation, and model training. This paper explores the architecture, tools, and best practices for developing robust and scalable ML data pipelines. It discusses key components such as data sources, ETL (Extract, Transform, Load) processes, storage solutions, and orchestration frameworks. The role of cloud platforms, distributed computing, and automation in optimizing pipeline performance is also examined. Additionally, best practices for data quality, monitoring, and versioning are highlighted to enhance reliability and reproducibility. By leveraging modern tools like Apache Airflow, Apache Spark, and Kubernetes, organizations can streamline their ML operations and improve scalability.},
      keywords = {Scalable Data Pipelines, Machine Learning, ETL, Data Orchestration, Cloud Computing, Apache Airflow, Apache Spark, Kubernetes, Automation},
      month = {January},
  }