Automation index
How much of a Biogas technician’s job can AI already do?
A methane reading comes earlier. Producing without the gasholder going up do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Biogas technician
19%
AI can already do 19% of this job.
#548 / 643
View rankingBiogas technician
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 |
|---|---|---|---|
Feed, methane and the log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Digester, the pH and the one that stops.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Gasholder, the seal and the leak.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Sludge, the fate and not mixing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The smell, the neighbour and not hiding the leak.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Produce, and not going up.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
- methane
- digesters
- gasholder
- sludge pH
- gas leaks
- plant feed
- smell
- seals
Sources for this job· O*NET 51-8099.00· ESCO/ISCO 3134
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