Overview

According to a 2019 report, 85% of AI projects fail to deliver on their intended promises to business. Why do so many projects fail?

Overview - ML Projects

Summary

  • ML is still research, therefore it is very challenging to aim for 100% success rate.

  • Many ML projects are technically infeasible or poorly scoped.

  • Many ML projects never make the leap into production.

  • Many ML projects have unclear success criteria.

  • Many ML projects are poorly managed.

PreviousSetting up Machine Learning ProjectsNextLifecycle

Last updated

Was this helpful?