Automation index
How much of a Pork butcher’s job can AI already do?
A marinade recipe comes earlier. Curing the loin and slicing the ham at the right point do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Pork butcher
21%
AI can already do 21% of this job.
#409 / 633
View rankingPork butcher
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 |
|---|---|---|---|
Marinade, cure and the salt point.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Break down, stuff and the tie.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Counter, slicing and the one who wants it thin.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Cold room, shrink and the batch that goes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The holiday, the order and not running out of chorizo.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
The ham’s point, and not selling it too dry.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- cure loins
- stuff sausage
- slice ham
- marinades
- tying
- curing room
- chorizo
- break down pork
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