Automatically generated by github-worflow[bot] for commit: cf656cc (#457)

Co-authored-by: iguazio-cicd <[email protected]>

Author: danielperezz

README.md

@@ -1,3 +1,47 @@
+### Change log [2025-11-25 12:39:14]
+1. Item Updated: `histogram_data_drift` (from version: `1.0.0` to `1.0.0`)
+2. Item Updated: `openai_proxy_app` (from version: `1.0.0` to `1.0.0`)
+3. Item Updated: `count_events` (from version: `1.0.0` to `1.0.0`)
+4. Item Updated: `evidently_iris` (from version: `1.0.0` to `1.0.0`)
+
+### Change log [2025-11-25 12:39:05]
+1. Item Updated: `test_classifier` (from version: `1.1.0` to `1.1.0`)
+2. Item Updated: `sklearn_classifier` (from version: `1.2.0` to `1.2.0`)
+3. Item Updated: `model_server_tester` (from version: `1.1.0` to `1.1.0`)
+4. Item Updated: `azureml_serving` (from version: `1.1.0` to `1.1.0`)
+5. Item Updated: `describe_dask` (from version: `1.2.0` to `1.2.0`)
+6. Item Updated: `batch_inference` (from version: `1.8.0` to `1.8.0`)
+7. Item Updated: `v2_model_server` (from version: `1.2.0` to `1.2.0`)
+8. Item Updated: `gen_class_data` (from version: `1.3.0` to `1.3.0`)
+9. Item Updated: `send_email` (from version: `1.2.0` to `1.2.0`)
+10. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`)
+11. Item Updated: `aggregate` (from version: `1.4.0` to `1.4.0`)
+12. Item Updated: `open_archive` (from version: `1.2.0` to `1.2.0`)
+13. Item Updated: `describe` (from version: `1.4.0` to `1.4.0`)
+14. Item Updated: `v2_model_tester` (from version: `1.1.0` to `1.1.0`)
+15. Item Updated: `text_to_audio_generator` (from version: `1.3.0` to `1.3.0`)
+16. Item Updated: `pii_recognizer` (from version: `0.4.0` to `0.4.0`)
+17. Item Updated: `github_utils` (from version: `1.1.0` to `1.1.0`)
+18. Item Updated: `sklearn_classifier_dask` (from version: `1.1.1` to `1.1.1`)
+19. Item Updated: `azureml_utils` (from version: `1.4.0` to `1.4.0`)
+20. Item Updated: `question_answering` (from version: `0.5.0` to `0.5.0`)
+21. Item Updated: `structured_data_generator` (from version: `1.6.0` to `1.6.0`)
+22. Item Updated: `arc_to_parquet` (from version: `1.5.0` to `1.5.0`)
+23. Item Updated: `silero_vad` (from version: `1.4.0` to `1.4.0`)
+24. Item Updated: `load_dataset` (from version: `1.2.0` to `1.2.0`)
+25. Item Updated: `auto_trainer` (from version: `1.8.0` to `1.8.0`)
+26. Item Updated: `feature_selection` (from version: `1.6.0` to `1.6.0`)
+27. Item Updated: `translate` (from version: `0.3.0` to `0.3.0`)
+28. Item Updated: `describe_spark` (from version: `1.1.0` to `1.1.0`)
+29. Item Updated: `pyannote_audio` (from version: `1.3.0` to `1.3.0`)
+30. Item Updated: `onnx_utils` (from version: `1.3.0` to `1.3.0`)
+31. Item Updated: `batch_inference_v2` (from version: `2.6.0` to `2.6.0`)
+32. Item Updated: `transcribe` (from version: `1.2.0` to `1.2.0`)
+33. Item Updated: `model_server` (from version: `1.2.0` to `1.2.0`)
+34. Item Updated: `mlflow_utils` (from version: `1.1.0` to `1.1.0`)
+35. Item Updated: `noise_reduction` (from version: `1.1.0` to `1.1.0`)
+36. Item Updated: `hugging_face_serving` (from version: `1.1.0` to `1.1.0`)
+
 ### Change log [2025-11-18 11:25:34]
 1. Item Updated: `histogram_data_drift` (from version: `1.0.0` to `1.0.0`)
 2. New item created: `openai_proxy_app` (version: `1.0.0`)

catalog.json

@@ -1 +1 @@
-{"categories": ["audio", "monitoring", "model-training", "deep-learning", "NLP", "model-testing", "machine-learning", "data-analysis", "genai", "data-generation", "utils", "data-preparation", "model-serving"], "kind": ["serving", "job", "nuclio:serving"]}
+{"categories": ["data-analysis", "data-generation", "model-training", "genai", "NLP", "model-testing", "utils", "monitoring", "deep-learning", "model-serving", "machine-learning", "audio", "data-preparation"], "kind": ["nuclio:serving", "job", "serving"]}

functions/development/tags.json

@@ -1 +1 @@
-{"count_events": {"latest": {"apiVersion": "v1", "categories": ["model-serving"], "description": "Count events in each time window", "example": "count_events.ipynb", "generationDate": "2025-09-16:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "count_events", "spec": {"filename": "count_events.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": null}, "version": "1.0.0", "assets": {"example": "src/count_events.ipynb", "source": "src/count_events.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving"], "description": "Count events in each time window", "example": "count_events.ipynb", "generationDate": "2025-09-16:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "count_events", "spec": {"filename": "count_events.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": null}, "version": "1.0.0", "assets": {"example": "src/count_events.ipynb", "source": "src/count_events.py", "docs": "static/documentation.html"}}}, "histogram_data_drift": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Model-monitoring application for detecting and visualizing data drift", "example": "histogram_data_drift.ipynb", "generationDate": "2025-11-06", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "histogram_data_drift", "spec": {"filename": "histogram_data_drift.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["plotly~=5.23", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/histogram_data_drift.ipynb", "source": "src/histogram_data_drift.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Model-monitoring application for detecting and visualizing data drift", "example": "histogram_data_drift.ipynb", "generationDate": "2025-11-06", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "histogram_data_drift", "spec": {"filename": "histogram_data_drift.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["plotly~=5.23", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/histogram_data_drift.ipynb", "source": "src/histogram_data_drift.py", "docs": "static/documentation.html"}}}, "openai_proxy_app": {"latest": {"apiVersion": "v1", "categories": ["genai"], "description": "OpenAI application runtime based on fastapi", "example": "openai_proxy_app.ipynb", "generationDate": "2025-11-11:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0", "name": "openai_proxy_app", "spec": {"filename": "openai_proxy_app.py", "image": "mlrun/mlrun", "requirements": ["fastapi>=0.110,<1.0", "requests>=2.31,<3.0"], "kind": "generic"}, "version": "1.0.0", "assets": {"example": "src/openai_proxy_app.ipynb", "source": "src/openai_proxy_app.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["genai"], "description": "OpenAI application runtime based on fastapi", "example": "openai_proxy_app.ipynb", "generationDate": "2025-11-11:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0", "name": "openai_proxy_app", "spec": {"filename": "openai_proxy_app.py", "image": "mlrun/mlrun", "requirements": ["fastapi>=0.110,<1.0", "requests>=2.31,<3.0"], "kind": "generic"}, "version": "1.0.0", "assets": {"example": "src/openai_proxy_app.ipynb", "source": "src/openai_proxy_app.py", "docs": "static/documentation.html"}}}, "evidently_iris": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset", "example": "evidently_iris.ipynb", "generationDate": "2025-11-09", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "evidently_iris", "spec": {"filename": "evidently_iris.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["scikit-learn~=1.5.2", "evidently~=0.7.5", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/evidently_iris.ipynb", "source": "src/evidently_iris.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset", "example": "evidently_iris.ipynb", "generationDate": "2025-11-09", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "evidently_iris", "spec": {"filename": "evidently_iris.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["scikit-learn~=1.5.2", "evidently~=0.7.5", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/evidently_iris.ipynb", "source": "src/evidently_iris.py", "docs": "static/documentation.html"}}}}
+{"count_events": {"latest": {"apiVersion": "v1", "categories": ["model-serving"], "description": "Count events in each time window", "example": "count_events.ipynb", "generationDate": "2025-09-16:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "count_events", "spec": {"filename": "count_events.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": null}, "version": "1.0.0", "assets": {"example": "src/count_events.ipynb", "source": "src/count_events.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving"], "description": "Count events in each time window", "example": "count_events.ipynb", "generationDate": "2025-09-16:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "count_events", "spec": {"filename": "count_events.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": null}, "version": "1.0.0", "assets": {"example": "src/count_events.ipynb", "source": "src/count_events.py", "docs": "static/documentation.html"}}}, "histogram_data_drift": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Model-monitoring application for detecting and visualizing data drift", "example": "histogram_data_drift.ipynb", "generationDate": "2025-11-06:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "histogram_data_drift", "spec": {"filename": "histogram_data_drift.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["plotly~=5.23", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/histogram_data_drift.ipynb", "source": "src/histogram_data_drift.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Model-monitoring application for detecting and visualizing data drift", "example": "histogram_data_drift.ipynb", "generationDate": "2025-11-06:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "histogram_data_drift", "spec": {"filename": "histogram_data_drift.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["plotly~=5.23", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/histogram_data_drift.ipynb", "source": "src/histogram_data_drift.py", "docs": "static/documentation.html"}}}, "openai_proxy_app": {"latest": {"apiVersion": "v1", "categories": ["genai"], "description": "OpenAI application runtime based on fastapi", "example": "openai_proxy_app.ipynb", "generationDate": "2025-11-11:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0", "name": "openai_proxy_app", "spec": {"filename": "openai_proxy_app.py", "image": "mlrun/mlrun", "requirements": ["fastapi>=0.110,<1.0", "requests>=2.31,<3.0"], "kind": "generic"}, "version": "1.0.0", "assets": {"example": "src/openai_proxy_app.ipynb", "source": "src/openai_proxy_app.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["genai"], "description": "OpenAI application runtime based on fastapi", "example": "openai_proxy_app.ipynb", "generationDate": "2025-11-11:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0", "name": "openai_proxy_app", "spec": {"filename": "openai_proxy_app.py", "image": "mlrun/mlrun", "requirements": ["fastapi>=0.110,<1.0", "requests>=2.31,<3.0"], "kind": "generic"}, "version": "1.0.0", "assets": {"example": "src/openai_proxy_app.ipynb", "source": "src/openai_proxy_app.py", "docs": "static/documentation.html"}}}, "evidently_iris": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset", "example": "evidently_iris.ipynb", "generationDate": "2025-11-09:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "evidently_iris", "spec": {"filename": "evidently_iris.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["scikit-learn~=1.5.2", "evidently~=0.7.5", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/evidently_iris.ipynb", "source": "src/evidently_iris.py", "docs": "static/documentation.html"}}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "structured-ML"], "description": "Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset", "example": "evidently_iris.ipynb", "generationDate": "2025-11-09:12-25", "hidden": false, "labels": {"author": "Iguazio"}, "mlrunVersion": "1.10.0-rc41", "name": "evidently_iris", "spec": {"filename": "evidently_iris.py", "image": "mlrun/mlrun", "kind": "monitoring_application", "requirements": ["scikit-learn~=1.5.2", "evidently~=0.7.5", "pandas"]}, "version": "1.0.0", "assets": {"example": "src/evidently_iris.ipynb", "source": "src/evidently_iris.py", "docs": "static/documentation.html"}}}}

modules/development/catalog.json

@@ -4,7 +4,7 @@ categories:
 - structured-ML
 description: Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset
 example: evidently_iris.ipynb
-generationDate: 2025-11-09    
+generationDate: 2025-11-09:12-25
 hidden: false                                       
 labels:
   author: Iguazio

modules/development/evidently_iris/1.0.0/src/item.yaml

@@ -34,7 +34,7 @@
 - structured-ML
 description: Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset
 example: evidently_iris.ipynb
-generationDate: 2025-11-09    
+generationDate: 2025-11-09:12-25
 hidden: false                                       
 labels:
   author: Iguazio

modules/development/evidently_iris/1.0.0/static/item.html

@@ -4,7 +4,7 @@ categories:
 - structured-ML
 description: Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset
 example: evidently_iris.ipynb
-generationDate: 2025-11-09    
+generationDate: 2025-11-09:12-25
 hidden: false                                       
 labels:
   author: Iguazio

modules/development/evidently_iris/latest/src/item.yaml

@@ -34,7 +34,7 @@
 - structured-ML
 description: Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset
 example: evidently_iris.ipynb
-generationDate: 2025-11-09    
+generationDate: 2025-11-09:12-25
 hidden: false                                       
 labels:
   author: Iguazio

modules/development/evidently_iris/latest/static/item.html

@@ -4,7 +4,7 @@ categories:
 - structured-ML
 description: Model-monitoring application for detecting and visualizing data drift
 example: histogram_data_drift.ipynb                  
-generationDate: 2025-11-06    
+generationDate: 2025-11-06:12-25
 hidden: false                                       
 labels:
   author: Iguazio

modules/development/histogram_data_drift/1.0.0/src/item.yaml

@@ -34,7 +34,7 @@
 - structured-ML
 description: Model-monitoring application for detecting and visualizing data drift
 example: histogram_data_drift.ipynb                  
-generationDate: 2025-11-06    
+generationDate: 2025-11-06:12-25
 hidden: false                                       
 labels:
   author: Iguazio