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DataOps
(Data Operations)
DataOps is an agile methodology for improving the quality and reducing the cycle time of data analytics. Inspired by DevOps principles, DataOps applies continuous integration and delivery to data pipelines, ensuring reliable data flows for ML and analytics.
This approach is particularly valuable in AI projects where data quality directly impacts model performance. DataOps incorporates elements of data engineering, ETL processes, and quality monitoring to create robust data supply chains. As organizations increasingly rely on data-driven decision making, DataOps practices help maintain the integrity and accessibility of critical data assets across their lifecycle.
This approach is particularly valuable in AI projects where data quality directly impacts model performance. DataOps incorporates elements of data engineering, ETL processes, and quality monitoring to create robust data supply chains. As organizations increasingly rely on data-driven decision making, DataOps practices help maintain the integrity and accessibility of critical data assets across their lifecycle.