Though the exact percentage was doubted, there was a broad agreement that the software project failure rate was unacceptably high. This led to the product management and development process evolution to improve the success rate.
High ML Project Failure Rate is Real
Even if the ML project failure rate is not ~80% (for example, failure at the Proof of Concept stage is a good thing), the “real” failure rate is quite likely still very high.
This story is repeated so very often:
A data scientist is brought in to do ML on data being collected.
She discovers that the data is unusable. Either nobody knows what it is, or it is incomplete and unreliable.
Somehow she manages to clean the data; experiment, and build a model. She has it all in her Jupyter notebook.
Management considers it done and ready to deploy. Only to learn that significant work is needed to take it to production.
Disappointed management says, “okay, do it.” The data scientist replies, “she can’t, engineers have to do it.” And engineers are like, “who, me? this math?”
Nobody is yet realizing that it is NOT done even after deployment. The model must be monitored for data drift and retrained.
Nobody is happy at the end. Management thinks ML is a hoax. Data Scientist thinks they don’t get it.