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Optimizing Distributed Data Processing for ML at Scale
This story was originally published on HackerNoon at: https://hackernoon.com/optimizing-distributed-data-processing-for-ml-at-scale. A practitioner's guide to ML data pipeline performance: read the query plan first, eliminate shuffle, fix file layout, handle skew, prune columns Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #spark, #pyspark, #machine-learning, #data-engineering, #performance-optimization, #distributed-systems, #distributed-data-processing, #optimizing-distributed-data, and more. This story was written by: @seshendranath. Learn more about this writer by checking @seshendranath's about page, and for more stories, please visit hackernoon.com. Stop tuning knobs on a broken foundation shuffle, file layout, skew, and column pruning do more for ML pipeline performance than any clever algorithm.
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