v1 ml app with ci/cd

[GokuMohandas]

No description provided.

[github-actions]

timestamp:

July 26, 2023 05:20:37 AM

run_id:

bc5f17a5428846bb97e14b3e6d3f49b6

params:

Key	Value
dropout_p	0.5
lr	0.0
lr_factor	0.8
lr_patience	3
num_samples	None
num_epochs	10
batch_size	256
num_classes	6

metrics:

epoch	train_loss	val_loss
0	5.383e-01	3.822e-01
1	3.738e-01	3.544e-01
2	3.296e-01	3.245e-01
3	2.839e-01	2.939e-01
4	2.462e-01	2.417e-01
5	2.130e-01	2.408e-01
6	1.670e-01	1.813e-01
7	1.357e-01	1.897e-01
8	1.090e-01	1.518e-01
9	8.273e-02	1.370e-01

[github-actions]

timestamp:

July 26, 2023 05:21:00 AM

run_id:

bc5f17a5428846bb97e14b3e6d3f49b6

overall:

Key	Value
precision	0.921
recall	0.916
f1	0.916
num_samples	191.0

per_class:

Key	Value
time-series	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'num_samples': 3.0}
reinforcement-learning	{'precision': 1.0, 'recall': 0.909, 'f1': 0.952, 'num_samples': 11.0}
computer-vision	{'precision': 0.969, 'recall': 0.887, 'f1': 0.926, 'num_samples': 71.0}
natural-language-processing	{'precision': 0.874, 'recall': 0.974, 'f1': 0.921, 'num_samples': 78.0}
graph-learning	{'precision': 1.0, 'recall': 0.833, 'f1': 0.909, 'num_samples': 12.0}
mlops	{'precision': 0.812, 'recall': 0.812, 'f1': 0.812, 'num_samples': 16.0}

slices:

Key	Value
nlp_llm	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'num_samples': 28}
short_text	{'precision': 0.857, 'recall': 0.857, 'f1': 0.857, 'num_samples': 7}