SIENTIAPDE-1645: Expose detailed model training information via a new Prometheus gauge. This gauge, sientia_training_info, records metadata such as dataset sizes, feature count, and evaluation metrics (MSE, MAE, R2) along with the training run's timestamp.

tests/test_metrics.py

@@ -329,3 +329,51 @@ def test_sientia_training_feature_count_is_gauge():
     assert isinstance(SIENTIA_TRAINING_FEATURE_COUNT, Gauge)
     assert SIENTIA_TRAINING_FEATURE_COUNT._name == 'sientia_training_feature_count'
     _assert_training_labels(SIENTIA_TRAINING_FEATURE_COUNT)
+
+
+def test_sientia_training_info_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_INFO
+
+    assert isinstance(SIENTIA_TRAINING_INFO, Gauge)
+    assert SIENTIA_TRAINING_INFO._name == 'sientia_training_info'
+
+    expected_labels = {
+        'pod_id', 'model_name', 'model_type',
+        'dataset_train_rows', 'dataset_val_rows', 'feature_count',
+        'mse', 'mae', 'r2',
+    }
+    assert expected_labels == set(SIENTIA_TRAINING_INFO._labelnames)
+
+
+def test_sientia_training_info_set_value():
+    import time
+
+    from model_manager.metrics import SIENTIA_TRAINING_INFO
+
+    ts = time.time() * 1000
+    SIENTIA_TRAINING_INFO.labels(
+        pod_id='test-pod',
+        model_name='my_model',
+        model_type='linear',
+        dataset_train_rows='1000',
+        dataset_val_rows='200',
+        feature_count='5',
+        mse='0.01',
+        mae='0.08',
+        r2='0.95',
+    ).set(ts)
+
+    value = SIENTIA_TRAINING_INFO.labels(
+        pod_id='test-pod',
+        model_name='my_model',
+        model_type='linear',
+        dataset_train_rows='1000',
+        dataset_val_rows='200',
+        feature_count='5',
+        mse='0.01',
+        mae='0.08',
+        r2='0.95',
+    )._value._value
+    assert abs(value - ts) < 2000