Automation index
How much of a Fisher’s job can AI already do?
A catch log and a route come earlier. The sea, the net, and a storm do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Fisher
15%
AI can already do 15% of this job.
#636 / 643
View rankingFisher
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 |
|---|---|---|---|
Catch logs, quotas and fishing GPS.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 32% | +4.5 |
Logged boat maintenance.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
Go out, set, haul and stow.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Read the sea, the gear and the school.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Safety, storm and fatigue.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Market, price and crew life.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 15% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Catch logs, quotas and fishing GPS
- Logged boat maintenance
- Go out, set, haul and stow
- Read the sea, the gear and the school
- Safety, storm and fatigue
- Market, price and crew life
- pescador
- fisher
- pesquero
Sources for this job· O*NET 45-3011.00· ESCO/ISCO 6222
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