Automation index
How much of a Weaver’s job can AI already do?
A weave comes earlier. Warping, weaving without breaking the pick do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Weaver
19%
AI can already do 19% of this job.
#542 / 633
View rankingWeaver
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 |
|---|---|---|---|
Warp, weave and the width.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Weave, shuttle and the yarn that snaps.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Finish, fringe and the shrink.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Loom, the rhythm and the commissioned piece.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The client, the colour and not changing the warp halfway.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Without breaking the pick, and a clean weave.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
- warp
- weaves
- shuttle
- looms
- fringe
- the pick
- cloth width
- shrink
Sources for this job· O*NET 51-6062.00· ESCO/ISCO 7318
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