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DESCRIPTION:Click for Latest Location Information: http://dgiq2023east.data
 versity.net/sessionPop.cfm?confid=158&proposalid=14753\nOne of the fundamen
 tal challenges for machine learning (ML) teams is data quality, or more acc
 urately the lack of data quality. Your ML solution is only as good as the d
 ata that you train it on, and therein lies the rub: Is your data of suffici
 ent quality to train a trustworthy system? If not, can you improve you
 r data so that it is? You need a collection of data quality “best pra
 ctices”, but what is “best” depends on the context of the
  problem that you face.  Which of the myriad of strategies are the bes
 t ones for you?\n\nThis presentation compares over a dozen traditional and 
 agile data quality techniques on five factors: timeliness of action, level 
 of automation, directness, timeliness of benefit, and difficulty to impleme
 nt. The data quality techniques explored include: data cleansing, auto
 mated regression testing, data guidance, synthetic training data, database 
 refactoring, data stewards, manual regression testing, data transformation,
  data masking, data labeling, and more. When you understand what data quali
 ty techniques are available to you, and understand the context in which the
 y’re applicable, you will be able to identify the collection of data 
 quality techniques that are best for you.\n
DTSTART:20231205T154500
SUMMARY:Techniques for Improving Data Quality: The Key to Machine Learning
DTEND:20231205T162959
LOCATION: See Description
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