Automation index
How much of a Ham carver’s job can AI already do?
A carving protocol comes earlier. Thin slices without wasting the leg do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Ham carver
19%
AI can already do 19% of this job.
#488 / 633
View rankingHam carver
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 |
|---|---|---|---|
Pick the leg, marbling and the cure point.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Slice, the hand and the one who wants it thick.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Use it, shrink and the bone.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Event, the plate and the bar rhythm.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The client, the photo and not making a show of it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Thin for real, and not wasting the cushion.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
- slice ham
- marbling
- the hand
- use the leg
- events
- the bone
- cure point
- the cushion
Sources for this job· O*NET 51-3021.00· ESCO/ISCO 7511
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