chore(readme): auto-update asset tables [skip ci]

Author: iguazio-cicd

functions/README.md

@@ -9,40 +9,40 @@ it is expected that contributors follow certain guidelines/protocols (please chi
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 | Name | Description | Kind | Categories |
 | --- | --- | --- | --- |
-| [aggregate](https://github.com/mlrun/functions/tree/development/functions/src/aggregate) | Rolling aggregation over Metrics and Lables according to specifications | job | data-preparation |
-| [arc_to_parquet](https://github.com/mlrun/functions/tree/development/functions/src/arc_to_parquet) | retrieve remote archive, open and save as parquet | job | utils |
-| [auto_trainer](https://github.com/mlrun/functions/tree/development/functions/src/auto_trainer) | Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM. | job | machine-learning, model-training |
-| [azureml_serving](https://github.com/mlrun/functions/tree/development/functions/src/azureml_serving) | AzureML serving function | serving | machine-learning, model-serving |
-| [azureml_utils](https://github.com/mlrun/functions/tree/development/functions/src/azureml_utils) | Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom. | job | model-serving, utils |
-| [batch_inference](https://github.com/mlrun/functions/tree/development/functions/src/batch_inference) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
-| [batch_inference_v2](https://github.com/mlrun/functions/tree/development/functions/src/batch_inference_v2) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
-| [describe](https://github.com/mlrun/functions/tree/development/functions/src/describe) | describe and visualizes dataset stats | job | data-analysis |
-| [describe_dask](https://github.com/mlrun/functions/tree/development/functions/src/describe_dask) | describe and visualizes dataset stats | job | data-analysis |
-| [describe_spark](https://github.com/mlrun/functions/tree/development/functions/src/describe_spark) |  | job | data-analysis |
-| [feature_selection](https://github.com/mlrun/functions/tree/development/functions/src/feature_selection) | Select features through multiple Statistical and Model filters | job | data-preparation, machine-learning |
-| [gen_class_data](https://github.com/mlrun/functions/tree/development/functions/src/gen_class_data) | Create a binary classification sample dataset and save. | job | data-generation |
-| [github_utils](https://github.com/mlrun/functions/tree/development/functions/src/github_utils) | add comments to github pull request | job | utils |
-| [hugging_face_serving](https://github.com/mlrun/functions/tree/development/functions/src/hugging_face_serving) | Generic Hugging Face model server. | serving | genai, model-serving |
-| [load_dataset](https://github.com/mlrun/functions/tree/development/functions/src/load_dataset) | load a toy dataset from scikit-learn | job | data-preparation |
-| [mlflow_utils](https://github.com/mlrun/functions/tree/development/functions/src/mlflow_utils) | Mlflow model server, and additional utils. | serving | model-serving, utils |
-| [model_server](https://github.com/mlrun/functions/tree/development/functions/src/model_server) | generic sklearn model server | nuclio:serving | model-serving, machine-learning |
-| [model_server_tester](https://github.com/mlrun/functions/tree/development/functions/src/model_server_tester) | test model servers | job | monitoring, model-serving |
-| [noise_reduction](https://github.com/mlrun/functions/tree/development/functions/src/noise_reduction) | Reduce noise from audio files | job | data-preparation, audio |
-| [onnx_utils](https://github.com/mlrun/functions/tree/development/functions/src/onnx_utils) | ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. | job | utils, deep-learning |
-| [open_archive](https://github.com/mlrun/functions/tree/development/functions/src/open_archive) | Open a file/object archive into a target directory | job | utils |
-| [pii_recognizer](https://github.com/mlrun/functions/tree/development/functions/src/pii_recognizer) | This function is used to recognize PII in a directory of text files | job | data-preparation, NLP |
-| [pyannote_audio](https://github.com/mlrun/functions/tree/development/functions/src/pyannote_audio) | pyannote's speech diarization of audio files | job | deep-learning, audio |
-| [question_answering](https://github.com/mlrun/functions/tree/development/functions/src/question_answering) | GenAI approach of question answering on a given data | job | genai |
-| [send_email](https://github.com/mlrun/functions/tree/development/functions/src/send_email) | Send Email messages through SMTP server | job | utils |
-| [silero_vad](https://github.com/mlrun/functions/tree/development/functions/src/silero_vad) | Silero VAD (Voice Activity Detection) functions. | job | deep-learning, audio |
-| [sklearn_classifier](https://github.com/mlrun/functions/tree/development/functions/src/sklearn_classifier) | train any classifier using scikit-learn's API | job | machine-learning, model-training |
-| [sklearn_classifier_dask](https://github.com/mlrun/functions/tree/development/functions/src/sklearn_classifier_dask) | train any classifier using scikit-learn's API over Dask | job | machine-learning, model-training |
-| [structured_data_generator](https://github.com/mlrun/functions/tree/development/functions/src/structured_data_generator) | GenAI approach of generating structured data according to a given schema | job | data-generation, genai |
-| [test_classifier](https://github.com/mlrun/functions/tree/development/functions/src/test_classifier) | test a classifier using held-out or new data | job | machine-learning, model-testing |
-| [text_to_audio_generator](https://github.com/mlrun/functions/tree/development/functions/src/text_to_audio_generator) | Generate audio file from text using different speakers | job | data-generation, audio |
-| [tf2_serving](https://github.com/mlrun/functions/tree/development/functions/src/tf2_serving) | tf2 image classification server | nuclio:serving | model-serving, machine-learning |
-| [transcribe](https://github.com/mlrun/functions/tree/development/functions/src/transcribe) | Transcribe audio files into text files | job | audio, genai |
-| [translate](https://github.com/mlrun/functions/tree/development/functions/src/translate) | Translate text files from one language to another | job | genai, NLP |
-| [v2_model_server](https://github.com/mlrun/functions/tree/development/functions/src/v2_model_server) | generic sklearn model server | serving | model-serving, machine-learning |
-| [v2_model_tester](https://github.com/mlrun/functions/tree/development/functions/src/v2_model_tester) | test v2 model servers | job | model-testing, machine-learning |
+| [aggregate](https://github.com/mlrun/functions/tree/master/functions/src/aggregate) | Rolling aggregation over Metrics and Lables according to specifications | job | data-preparation |
+| [arc_to_parquet](https://github.com/mlrun/functions/tree/master/functions/src/arc_to_parquet) | retrieve remote archive, open and save as parquet | job | utils |
+| [auto_trainer](https://github.com/mlrun/functions/tree/master/functions/src/auto_trainer) | Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM. | job | machine-learning, model-training |
+| [azureml_serving](https://github.com/mlrun/functions/tree/master/functions/src/azureml_serving) | AzureML serving function | serving | machine-learning, model-serving |
+| [azureml_utils](https://github.com/mlrun/functions/tree/master/functions/src/azureml_utils) | Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom. | job | model-serving, utils |
+| [batch_inference](https://github.com/mlrun/functions/tree/master/functions/src/batch_inference) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
+| [batch_inference_v2](https://github.com/mlrun/functions/tree/master/functions/src/batch_inference_v2) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
+| [describe](https://github.com/mlrun/functions/tree/master/functions/src/describe) | describe and visualizes dataset stats | job | data-analysis |
+| [describe_dask](https://github.com/mlrun/functions/tree/master/functions/src/describe_dask) | describe and visualizes dataset stats | job | data-analysis |
+| [describe_spark](https://github.com/mlrun/functions/tree/master/functions/src/describe_spark) |  | job | data-analysis |
+| [feature_selection](https://github.com/mlrun/functions/tree/master/functions/src/feature_selection) | Select features through multiple Statistical and Model filters | job | data-preparation, machine-learning |
+| [gen_class_data](https://github.com/mlrun/functions/tree/master/functions/src/gen_class_data) | Create a binary classification sample dataset and save. | job | data-generation |
+| [github_utils](https://github.com/mlrun/functions/tree/master/functions/src/github_utils) | add comments to github pull request | job | utils |
+| [hugging_face_serving](https://github.com/mlrun/functions/tree/master/functions/src/hugging_face_serving) | Generic Hugging Face model server. | serving | genai, model-serving |
+| [load_dataset](https://github.com/mlrun/functions/tree/master/functions/src/load_dataset) | load a toy dataset from scikit-learn | job | data-preparation |
+| [mlflow_utils](https://github.com/mlrun/functions/tree/master/functions/src/mlflow_utils) | Mlflow model server, and additional utils. | serving | model-serving, utils |
+| [model_server](https://github.com/mlrun/functions/tree/master/functions/src/model_server) | generic sklearn model server | nuclio:serving | model-serving, machine-learning |
+| [model_server_tester](https://github.com/mlrun/functions/tree/master/functions/src/model_server_tester) | test model servers | job | monitoring, model-serving |
+| [noise_reduction](https://github.com/mlrun/functions/tree/master/functions/src/noise_reduction) | Reduce noise from audio files | job | data-preparation, audio |
+| [onnx_utils](https://github.com/mlrun/functions/tree/master/functions/src/onnx_utils) | ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. | job | utils, deep-learning |
+| [open_archive](https://github.com/mlrun/functions/tree/master/functions/src/open_archive) | Open a file/object archive into a target directory | job | utils |
+| [pii_recognizer](https://github.com/mlrun/functions/tree/master/functions/src/pii_recognizer) | This function is used to recognize PII in a directory of text files | job | data-preparation, NLP |
+| [pyannote_audio](https://github.com/mlrun/functions/tree/master/functions/src/pyannote_audio) | pyannote's speech diarization of audio files | job | deep-learning, audio |
+| [question_answering](https://github.com/mlrun/functions/tree/master/functions/src/question_answering) | GenAI approach of question answering on a given data | job | genai |
+| [send_email](https://github.com/mlrun/functions/tree/master/functions/src/send_email) | Send Email messages through SMTP server | job | utils |
+| [silero_vad](https://github.com/mlrun/functions/tree/master/functions/src/silero_vad) | Silero VAD (Voice Activity Detection) functions. | job | deep-learning, audio |
+| [sklearn_classifier](https://github.com/mlrun/functions/tree/master/functions/src/sklearn_classifier) | train any classifier using scikit-learn's API | job | machine-learning, model-training |
+| [sklearn_classifier_dask](https://github.com/mlrun/functions/tree/master/functions/src/sklearn_classifier_dask) | train any classifier using scikit-learn's API over Dask | job | machine-learning, model-training |
+| [structured_data_generator](https://github.com/mlrun/functions/tree/master/functions/src/structured_data_generator) | GenAI approach of generating structured data according to a given schema | job | data-generation, genai |
+| [test_classifier](https://github.com/mlrun/functions/tree/master/functions/src/test_classifier) | test a classifier using held-out or new data | job | machine-learning, model-testing |
+| [text_to_audio_generator](https://github.com/mlrun/functions/tree/master/functions/src/text_to_audio_generator) | Generate audio file from text using different speakers | job | data-generation, audio |
+| [tf2_serving](https://github.com/mlrun/functions/tree/master/functions/src/tf2_serving) | tf2 image classification server | nuclio:serving | model-serving, machine-learning |
+| [transcribe](https://github.com/mlrun/functions/tree/master/functions/src/transcribe) | Transcribe audio files into text files | job | audio, genai |
+| [translate](https://github.com/mlrun/functions/tree/master/functions/src/translate) | Translate text files from one language to another | job | genai, NLP |
+| [v2_model_server](https://github.com/mlrun/functions/tree/master/functions/src/v2_model_server) | generic sklearn model server | serving | model-serving, machine-learning |
+| [v2_model_tester](https://github.com/mlrun/functions/tree/master/functions/src/v2_model_tester) | test v2 model servers | job | model-testing, machine-learning |
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modules/README.md

@@ -6,10 +6,10 @@
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 | Name | Description | Kind | Categories |
 | --- | --- | --- | --- |
-| [agent_deployer](https://github.com/mlrun/functions/tree/development/modules/src/agent_deployer) | Helper for serving function deploy of an AI agents using MLRun | monitoring_application | model-serving |
-| [count_events](https://github.com/mlrun/functions/tree/development/modules/src/count_events) | Count events in each time window | monitoring_application | model-serving |
-| [evidently_iris](https://github.com/mlrun/functions/tree/development/modules/src/evidently_iris) | Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset | monitoring_application | model-serving, structured-ML |
-| [histogram_data_drift](https://github.com/mlrun/functions/tree/development/modules/src/histogram_data_drift) | Model-monitoring application for detecting and visualizing data drift | monitoring_application | model-serving, structured-ML |
-| [openai_proxy_app](https://github.com/mlrun/functions/tree/development/modules/src/openai_proxy_app) | OpenAI application runtime based on fastapi | generic | genai |
-| [vllm_module](https://github.com/mlrun/functions/tree/development/modules/src/vllm_module) | Deploys a vLLM OpenAI-compatible LLM server as an MLRun application runtime, with configurable GPU usage, node selection, tensor parallelism, and runtime flags. | generic | genai |
+| [agent_deployer](https://github.com/mlrun/functions/tree/master/modules/src/agent_deployer) | Helper for serving function deploy of an AI agents using MLRun | monitoring_application | model-serving |
+| [count_events](https://github.com/mlrun/functions/tree/master/modules/src/count_events) | Count events in each time window | monitoring_application | model-serving |
+| [evidently_iris](https://github.com/mlrun/functions/tree/master/modules/src/evidently_iris) | Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset | monitoring_application | model-serving, structured-ML |
+| [histogram_data_drift](https://github.com/mlrun/functions/tree/master/modules/src/histogram_data_drift) | Model-monitoring application for detecting and visualizing data drift | monitoring_application | model-serving, structured-ML |
+| [openai_proxy_app](https://github.com/mlrun/functions/tree/master/modules/src/openai_proxy_app) | OpenAI application runtime based on fastapi | generic | genai |
+| [vllm_module](https://github.com/mlrun/functions/tree/master/modules/src/vllm_module) | Deploys a vLLM OpenAI-compatible LLM server as an MLRun application runtime, with configurable GPU usage, node selection, tensor parallelism, and runtime flags. | generic | genai |
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