I would almost state &quot;Clean datasets (like MNIST) considered harmful&quot; because without exception the data scientists I meet who work with them just shovel real data blindly into ML code and kvetch when they don&#x27;t beat simpler existing approaches right out of the gate.<p>And without fail so far, just cleaning up the data (feature-scaling, removing input errors etc) changes this.  I vacillate on building an automagic tool to do this for them, but IMO such a tool would send tsunamis of technical debt up their data pipelines as they got even sloppier about data until the tool was overwhelmed by stuff it couldn&#x27;t detect.  I&#x27;d much rather handle the lesser of two evils of them getting nowhere until they realize this isn&#x27;t magic.