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Communities & Learning
AI Data Quality Focus
★ 4.3
Technical Data Conference
★ 4.4
N/A — web platformN/A — web platformN/A — web platformN/A — web platformPython data engineers involved in ML pipeline development use the Data-Centric AI community to learn systematic approaches to improving training data quality. Techniques like slice-based evaluation, programmatic data labeling with Snorkel, and error analysis tools inform how engineers build data cleaning stages in their Python ML pipelines.
Python data engineers attend Data Council to see how leading engineering teams design their Python-based data infrastructure at scale. Conference talks on PySpark optimization, Airflow at scale, and Python ML pipeline architecture provide engineering depth rarely covered in blog posts, and the networking sessions connect practitioners across companies.
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