Automation index
How much of a Cider maker’s job can AI already do?
A must analysis comes earlier. Fermenting and pouring at the point do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Cider maker
22%
AI can already do 22% of this job.
#379 / 633
View rankingCider maker
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 |
|---|---|---|---|
Apple, mill and the must.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Press, ferment and the one that sours.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Rack, the gas and the pour point.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The cellar, this year’s cider and the bar.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The festival, the public and not tapping early.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
At the point, and not selling the sour.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- mill apples
- the press
- ferment cider
- pour from height
- rack
- the cider bar
- must
- barrels
Sources for this job· O*NET 51-9012.00· ESCO/ISCO 8160
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