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He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. A transcript of the talk follows.
He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. A transcript of the talk follows.
Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data.
The following figure represents the life cycle of data science. It starts with gathering the business requirements and relevant data. Once the data is acquired, it is maintained by performing datacleaning, data warehousing, data staging, and data architecture. Why is datacleaning crucial?
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