Automation index
How much of a Chimney sweep’s job can AI already do?
A inspection protocol comes earlier. Climbing, cleaning and not setting the soot on fire do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Chimney sweep
19%
AI can already do 19% of this job.
#490 / 633
View rankingChimney sweep
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 |
|---|---|---|---|
Inspection, camera and the flue report.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Climb, sweep and the nest that blocks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Tightness, draught and the one that will not draw.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Certificate, rules and the residents.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The wet roof and not falling.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Clean for real, and not leaving loose soot.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
- sweep chimneys
- flue camera
- draught
- flue certificate
- nests
- roofs
- tightness
- soot
Sources for this job· O*NET 47-4071.00· ESCO/ISCO 7133
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