Automation index
How much of a Yacht skipper’s job can AI already do?
A leisure course comes earlier. Bringing friends and boat whole do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Yacht skipper
19%
AI can already do 19% of this job.
#535 / 643
View rankingYacht skipper
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 |
|---|---|---|---|
Course, weather and the day’s plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Helm, the sea and the manoeuvre.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Crew, the sick one and the novice.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Breakdown, sail or engine and the call.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The charter, the client and not pushing it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Whole into harbour, and not trusting the plotter.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
- leisure course
- helm
- weather
- harbour manoeuvre
- novices
- yacht charter
- breakdowns
- plotter
Sources for this job· O*NET 53-5021.00· ESCO/ISCO 3152
IS WORK OVER?
ML engineer
29%
#64 / 643
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