data quality is important. every ai project i've worked on has started with visualizing the data and thinking about it. it's easy to get complacent and focus o

data quality is important. every ai project i've worked on has started with visualizing the data and thinking about it. it's easy to get complacent and focus on building big datasets. in practice, looking at the data often reveals issues sometimes in data quality and sometimes scope of what's in there (if you're missing key examples, it's simply not going to work). most ml is actually data engineering.