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DataOps for Data Science and MLOps

The conventional wisdom for data science is that more data is better. However, this is not always true. In many cases, a smaller but clean data set can result in better model performance than a large noisy data set. But how can we get to a clean data set given the inherent variability in human behavior that produces most data sets? That’s where DataOps comes in. DataOps is an emerging, proactive approach to data management that focuses on orchestrating people, processes, and technology to quickly deliver high-quality data to data consumers. This talk will explain what DataOps is in more detail, and how it can integrate with MLOps to help data scientists produce and manage better models, no matter what size of data set.