- 74% to 87% of ML projects fail or don’t reach production. Reasons:
- Lack of experienced talent.
- Lack of support by leadership
- Missing data infrastructure.
- Data labelling challenges
- Siloed organizations and lack of collaboration
- Technically infeasible projects
- Lack of alignment between technical and business teams.
When to (not) Use ML