Why data quality is key to successful ML Ops

Machine learning has been, and will continue to be, one of the biggest topics in data for the foreseeable future. And while we in the data community are all still riding the high of discovering and tuning predictive algorithms that can tell us whether a picture shows a dog or a blueberry muffin, we're also beginning to realize that ML isn't just a magic wand you can wave at a pile of data to quickly get insightful, reliable results. Instead, we are starting to treat ML like other software engineering disciplines that require processes and tooling to ensure seamless workflows and reliable outputs. "Poor data quality is Enemy #1 to the widespread, profitable use of machine learning, and for this reason, the growth of machine learning increases the importance of data cleansing and preparation. The quality demands of machine learning are steep, and bad data can backfire twice -- first when training predictive models and second in the new data used by that model to inform future decisions."