Automation index
How much of a Beekeeper’s job can AI already do?
A hive log comes earlier. Checking without angering them and taking honey do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Beekeeper
21%
AI can already do 21% of this job.
#407 / 633
View rankingBeekeeper
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 |
|---|---|---|---|
Check, queen and the hive log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Honey, supers and the smoker.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Varroa, treatments and not losing the stand.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Moving hives, bloom and the move.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The neighbour, the sting and the field permit.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Take honey, and not leaving them without stores.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- check hives
- supers
- varroa
- moving hives
- smoker
- queen
- bloom
- the stand
Sources for this job· O*NET 45-2092.00· ESCO/ISCO 6123
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