Automation index
How much of a Ceramicist’s job can AI already do?
A firing curve comes earlier. Glazing without losing the batch in the firing do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Ceramicist
19%
AI can already do 19% of this job.
#484 / 633
View rankingCeramicist
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 |
|---|---|---|---|
Body, mould and the curve.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Glaze, apply and the one that runs.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Kiln, atmosphere and the batch that sticks.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Run, the order and the one that does not match.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The gallery, the deadline and not opening the kiln early.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Not losing the batch, and signing the curve.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
- firing curves
- bodies
- ceramic moulds
- glazes
- atmosphere
- runs
- runs of glaze
- the firing
Sources for this job· O*NET 51-9195.00· ESCO/ISCO 7314
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