Automation index
How much of a Milliner’s job can AI already do?
A block comes earlier. Sitting on the head without flying at the first breeze do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Milliner
19%
AI can already do 19% of this job.
#543 / 643
View rankingMilliner
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 |
|---|---|---|---|
Block, crown and the material.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Shape, stitch and the brim that will not sit.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Fit, the hair and the one that will not take a pin.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Trim, feather and the balance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The wedding, the wind and not making a prop hat.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
That it sits, and does not fly.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- hat blocks
- crowns
- brims
- feathers
- pins
- fit hats
- trim
- balance
Sources for this job· O*NET 51-6051.00· ESCO/ISCO 7531
IS WORK OVER?
ML engineer
29%
#64 / 643
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