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Summary: Dataquality is a fundamental aspect of Machine Learning. Poor-qualitydata leads to biased and unreliable models, while high-qualitydata enables accurate predictions and insights. What is DataQuality in Machine Learning? Bias in data can result in unfair and discriminatory outcomes.
Ensuring high-qualitydata A crucial aspect of downstream consumption is dataquality. Studies have shown that 80% of time is spent on datapreparation and cleansing, leaving only 20% of time for data analytics. This leaves more time for data analysis.
See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from datapreparation and model development to deployment and monitoring. Data monitoring tools help monitor the quality of the data.
This satisfies the needs of data owners, who require a simple way to make data products available to users and keep them up to date, and data users who demand user-friendly, self-service methods for finding and accessing trusted data.
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