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.

Course Content - Previous

Setting up Machine Learning Projects

Lifecycle

Last modified 2yr ago