Automation index
How much of a Tennis coach’s job can AI already do?
A stroke pattern comes earlier. Getting them on court without wrecking the shoulder do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Tennis coach
19%
AI can already do 19% of this job.
#495 / 633
View rankingTennis coach
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 |
|---|---|---|---|
Pattern, rally and the plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Court, the one who hides and the one who overhits.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Competition, the needle and the one who throws the racquet.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Shoulder, load and not rushing the event.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Parents, the ranking and not burning the child.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
On court, and the shoulder whole.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
- rally
- stroke patterns
- court
- shoulder
- ranking
- racquet
- parents
- training load
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