Automation index
How much of a Golf professional’s job can AI already do?
A swing clip comes earlier. Changing the strike so the handicap comes down do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Golf professional
22%
AI can already do 22% of this job.
#369 / 633
View rankingGolf professional
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 |
|---|---|---|---|
Swing, clip and the plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Course, the pupil and the one who does not listen.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Putting, the green and patience.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Handicap, the event and not inflating.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The shop, the club and not selling smoke.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Bring the handicap down, and not breaking the swing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- swing
- strike clip
- putting
- handicap
- the green
- events
- clubs
- the range
Sources for this job· O*NET 27-2022.00· 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