Automation index
How much of a Stained-glass artist’s job can AI already do?
A window cartoon comes earlier. Leading, soldering without letting the air in do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Stained-glass artist
19%
AI can already do 19% of this job.
#558 / 643
View rankingStained-glass artist
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 |
|---|---|---|---|
Cartoon, cut and the glass colour.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Lead, solder and the one that opens.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Cement, clean and the weatherseal.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Opening, the church and the scaffold.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The restoration, the original and not inventing extra.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
No air in, and firm lead.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
- window cartoon
- lead
- solder
- cement
- cut glass
- openings
- restore windows
- weatherseal
Sources for this job· O*NET 51-9195.00· ESCO/ISCO 7315
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