Automation index
How much of a Ski instructor’s job can AI already do?
A turn method comes earlier. Getting down the run without taking anyone with them do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Ski instructor
19%
AI can already do 19% of this job.
#512 / 633
View rankingSki 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 |
|---|---|---|---|
Level, kit and the group.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Run, the turn and the one who cuts across.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Fall, the one who cries and getting them up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Snow, the storm and closing in time.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The queue, the pass and not losing a child.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Down whole, and taking nobody with them.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
- turns
- runs
- falls
- snow groups
- the pass
- the storm
- get them up
- ski levels
Sources for this job· O*NET 39-9032.00· 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