Automation index
How much of a Diving instructor’s job can AI already do?
A dive briefing comes earlier. Coming up whole without forgetting to breathe do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Diving instructor
19%
AI can already do 19% of this job.
#503 / 633
View rankingDiving instructor
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 |
|---|---|---|---|
Briefing, tables and the plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Water, the student and the one who panics.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Drills, the regulator and the one who is out of air.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Log, certification and not giving the star away.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The current, the vis and aborting.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Come up whole, and not skipping the stop.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
- dive briefing
- tables
- regulator
- certify divers
- current
- visibility
- stops
- nervous students
Sources for this job· O*NET 25-3021.00· ESCO/ISCO 3422
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