Automation index
How much of a Camera operator’s job can AI already do?
A schedule and an overlay come earlier. The shot you have to nail does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Camera operator
18%
AI can already do 18% of this job.
#566 / 643
View rankingCamera operator
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 |
|---|---|---|---|
Schedule, lists and overlays.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Frame, follow and not lose focus.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Kit, cards and backups.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Director, light and sound.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Knees, shoulder and long days.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not wiping a full card.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Schedule, lists and overlays
- Frame, follow and not lose focus
- Kit, cards and backups
- Director, light and sound
- Knees, shoulder and long days
- Not wiping a full card
- camara
- operador de camara
- camera operator
Sources for this job· O*NET 27-4031.00· ESCO/ISCO 3521
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