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Machine Learning (ML) is a powerful tool that can be used to solve a wide variety of problems. Getting your ML model ready for action: This stage involves building and training a machine learning model using efficient machine learning algorithms. Cleaningdata: Once the data has been gathered, it needs to be cleaned.
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Piyush Puri: Please join me in welcoming to the stage our next speakers who are here to talk about data-centric AI at Capital One, the amazing team who may or may not have coined the term, “what’s in your wallet.” What can get less attention is the foundational element of what makes AI and ML shine. That’s data.
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As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our datapipelines. So why should we use datapipelines?
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