IS WORK OVER?

Automation index

How much of a ML engineer’s job can AI already do?

Training a baseline and a notebook is fast. Shipping it, watching drift, and owning the miss is not.

This percentage does not predict how many people will lose their jobs.

Task comparison

ML 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.

TaskShareAIPoints
Train models, features and notebooks.
Estimate

No public source provides a precise figure. This is our estimate.

22%48%+10.6
Evaluate, document and compare runs.
Estimate

No public source provides a precise figure. This is our estimate.

18%42%+7.6
Deploy, monitor and retrain.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Talk to product about what a good error is.
Estimate

No public source provides a precise figure. This is our estimate.

16%15%+2.4
Dirty data, bias and model limits.
Estimate

No public source provides a precise figure. This is our estimate.

14%18%+2.5
On-call, incidents and rollback.
Estimate

No public source provides a precise figure. This is our estimate.

14%16%+2.2
Total100%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

Blood bank 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.

TaskShareAIPoints
Group, screen and the bag crossmatch.
Estimate

No public source provides a precise figure. This is our estimate.

18%52%+9.4
Fractionate, label and traceability.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
The emergency, the massive and the ID error.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
Donation, apheresis and the donor who feels unwell.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
Stock, expiry and what theatre asks for.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
The right bag and owning the blood group.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%28%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • crossmatch blood
  • blood group
  • fractionate products
  • donor apheresis
  • bag traceability
  • massive transfusion
  • label units
  • blood bank

Sources for this job· O*NET 29-2011.00· ESCO/ISCO 3212

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