Sample AutoML report

This section contains a sample AutoML report generated during CI.

The CI is set up as follows:

AutoML statistics

  • Optimized metric: f1

  • The number of generated models: 55

  • The number of trained and evaluated models: 43

  • The number of successful training processes: 52

  • The number of models that caused a crash: 0

  • The number of models that failed due to the timeout: 0

  • The number of models that failed due to the too large size: 3

  • The number of models that failed due to incompatibility: 0

Training overview

Bokeh Plot

Figure 12 Loss value during AutoML training process

Bokeh Plot

Figure 13 Comparison of loss value across models

Summary of generated models

Bokeh Plot

Figure 14 Metrics of models trained by AutoML flow

Table 5 Summary of generated models’ parameters

Model ID

Number of layers

Optimized model size [KB]

Total parameters

Trainable parameters

3

7

15.5546875

2815

2814

4

10

49.73046875

11623

11622

5

17

33.08203125

7498

7497

6

21

33.4921875

7613

7612

7

27

61.02734375

14094

14093

8

14

50.16015625

11841

11840

9

21

35.60546875

7834

7833

10

17

37.4296875

8732

8731

11

23

54.3359375

11691

11690

12

21

30.37890625

7656

7655

13

17

33.13671875

7799

7798

14

12

37.5625

8227

8226

15

10

42.421875

10264

10263

16

13

45.5546875

10455

10454

17

8

19.8671875

4364

4363

18

27

47.5078125

10720

10719

19

10

46.0703125

11170

11169

20

11

32.00390625

6570

6569

21

13

32.59375

7850

7849

22

10

34.84375

7914

7913

23

14

46.69921875

10981

10980

24

23

47.4765625

11121

11120

25

11

36.171875

8584

8583

26

15

21.55078125

4368

4367

27

11

24.56640625

4681

4680

28

19

46.17578125

10569

10568

29

11

34.19140625

7146

7145

30

11

38.6640625

8295

8294

31

9

35.125

8078

8077

32

13

42.62890625

10440

10439

33

13

18.5390625

4599

4598

34

21

52.78125

12469

12468

35

10

38.8203125

9133

9132

36

11

51.62109375

11832

11831

37

23

34.12890625

7143

7142

38

15

24.7421875

5204

5203

39

25

45.21875

9813

9812

40

12

45.75390625

9549

9548

41

19

24.55078125

5610

5609

42

19

23.66796875

5519

5518

43

11

38.98046875

8819

8818

44

12

24.74609375

4710

4709

45

19

40.88671875

9374

9373

46

15

38.5703125

9920

9919

47

23

38.38671875

8480

8479

48

11

37.28515625

7881

7880

Classification comparison

Comparison of inference time, F1 score and model size

Bokeh Plot

Figure 15 Model size, speed and quality summary. The F1 score of the model is presented on Y axis. The inference time of the model is presented on X axis. The size of the model is represented by the size of its point.

Table 6 Comparison of model inference time, accuracy and size

Model name

Mean Inference time [s]

Size [MB]

F1 score

workspace.automl-results.1234_12_5.0.measurements.json

0.000844

0.032

0.250000

workspace.automl-results.1234_37_5.0.measurements.json

0.000923

0.035

0.250000

workspace.automl-results.1234_3_5.0.measurements.json

0.000409

0.016

0.250000

workspace.automl-results.1234_16_5.0.measurements.json

0.001246

0.047

0.250000

workspace.automl-results.1234_41_5.0.measurements.json

0.000558

0.025

0.250000

Detailed metrics comparison

Bokeh Plot

Figure 16 Radar chart representing the accuracy, precision and recall for models

Table 7 Summary of classification metrics for models

Model name

Accuracy

F1 score

G-mean

Mean precision

Mean sensitivity

ROC AUC

workspace.automl-results.1234_12_5.0.measurements.json

0.952000

0.250000

0.406529

0.729675

0.579132

0.579132

workspace.automl-results.1234_37_5.0.measurements.json

0.952000

0.250000

0.406529

0.729675

0.579132

0.579132

workspace.automl-results.1234_3_5.0.measurements.json

0.952000

0.250000

0.406529

0.729675

0.579132

0.579132

workspace.automl-results.1234_16_5.0.measurements.json

0.952000

0.250000

0.406529

0.729675

0.579132

0.579132

workspace.automl-results.1234_41_5.0.measurements.json

0.952000

0.250000

0.406529

0.729675

0.579132

0.579132

Inference comparison

Performance metrics

Bokeh Application

Figure 17 Plot represents changes of inference time over time for all models.

Table 8 Summary of inference time metrics for models

Model name

Minimum [s]

Standard deviation [s]

Mean [s]

Median [s]

Maximum [s]

workspace.automl-results.1234_12_5.0.measurements.json

0.000823

0.000010

0.000844

0.000844

0.000951

workspace.automl-results.1234_37_5.0.measurements.json

0.000900

0.000008

0.000923

0.000923

0.000955

workspace.automl-results.1234_3_5.0.measurements.json

0.000389

0.000007

0.000409

0.000408

0.000453

workspace.automl-results.1234_16_5.0.measurements.json

0.001229

0.000008

0.001246

0.001245

0.001342

workspace.automl-results.1234_41_5.0.measurements.json

0.000542

0.000011

0.000558

0.000556

0.000659

Mean comparison plots

Bokeh Plot

Figure 18 Violin chart representing distribution of values for performance metrics for models

Table 9 Performance metric for models

Model name

Inference time [s]

workspace.automl-results.1234_12_5.0.measurements.json

0.000844

workspace.automl-results.1234_37_5.0.measurements.json

0.000923

workspace.automl-results.1234_3_5.0.measurements.json

0.000409

workspace.automl-results.1234_16_5.0.measurements.json

0.001246

workspace.automl-results.1234_41_5.0.measurements.json

0.000558
Last update: 2026-06-25