Automation index
How much of a Dry cleaner’s job can AI already do?
A stain card comes earlier. Lifting the stain without eating the colour do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Dry cleaner
22%
AI can already do 22% of this job.
#360 / 633
View rankingDry cleaner
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Card, fibre and pretreatment.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Machine, solvent and the delicate garment.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Press, the finish and the one that marks.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Job, the groom and the one who wants it now.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The old stain and not promising miracles.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Lift the stain, and not eating the colour.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- pretreat stains
- solvents
- press
- delicates
- job cards
- finish
- fibres
- old stains
Sources for this job· O*NET 51-6011.00· ESCO/ISCO 9121
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511