I’ll bite. I worked at a very large Fortune 500 company for a few years. Through some twist of fate, I ended up on an overfunded ML organization that was under no pressure to produce anything of business value. The executive in charge of this operation let this fly for about a year before they realized they weren’t getting anything valuable out of it. They put pressure on the VP of the org to start producing business value or else.
Fast forward another month or so and there are six hastily thrown together initiatives in which our organization is contracted to other organizations to “help them out”. The team dynamics were atrocious - our team was met with skepticism as to why we were helping out (was the other team not doing their job well enough?), and people on our team were often clueless on the business subject matter that we were supposed to be consulting on.
I was assigned to one of these initiatives, and upon digging into the business problem, our team realized that the feature importance was completely contained within a single categorical feature in the model. All other features in the model were comparable to uniformly randomly generated features. In the selfish interest of being able to claim that they “solved their problem with ML”, it was clear that either the model developer or the team at large had obscured this fact.
In an effort to save face, the VP kept us on the project, despite the poor relations and lack of useful features to improve the model. We didn’t improve the model and eventually, the VP ended up being “pushed out”. Last I checked, this VP is currently the CEO of a startup that recently raised a $100M seed round.
Fast forward another month or so and there are six hastily thrown together initiatives in which our organization is contracted to other organizations to “help them out”. The team dynamics were atrocious - our team was met with skepticism as to why we were helping out (was the other team not doing their job well enough?), and people on our team were often clueless on the business subject matter that we were supposed to be consulting on.
I was assigned to one of these initiatives, and upon digging into the business problem, our team realized that the feature importance was completely contained within a single categorical feature in the model. All other features in the model were comparable to uniformly randomly generated features. In the selfish interest of being able to claim that they “solved their problem with ML”, it was clear that either the model developer or the team at large had obscured this fact.
In an effort to save face, the VP kept us on the project, despite the poor relations and lack of useful features to improve the model. We didn’t improve the model and eventually, the VP ended up being “pushed out”. Last I checked, this VP is currently the CEO of a startup that recently raised a $100M seed round.