Creating and putting in use effective data pipeline for learning processes
Keywords:
Apache Kafka, Apache Airflow, big dataAbstract
Operation of machine learning (ML) depends on effective data pipelines. Expanding
databases in size and complexity calls for more effective data transport, transformation, and
accessibility as well. Emphasizing speed, scalability, and dependability, this work
investigates fundamental methods for the design and implementation of data pipelines that
improve machine learning processes. Essential for maintaining pipeline resilience and
efficiency, we stress ideal approaches including modular pipeline architecture, versioning,
data validation, and monitoring. Combining cloud infrastructure, distributed computing
models, and data orchestration tools helps to maximize complex activities. The paper
examines the problems data engineers face include guaranteeing low-latency access,
storing solutions optimization, and data missing or inconsistent data management. Empirical
case studies show how well-built data pipelines help to reduce resource costs and improve
process efficiency. Fast data pipelines are ultimately basic for efficient machine learning
implementation as they let data scientists focus on model construction rather than data
manipulation. Professionals looking to build pipelines that fit the dynamic needs of modern
machine-learning applications find a framework in this talk.
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