# Azure Machine Learning Model Monitoring SDK
The `azure-ai-mlmonitoring` package provides an SDK to enable Model Data Collector (MDC) for custom logging allows customers to collect data at arbitrary points in their data pre-processing pipeline. Customers can leverage SDK in `score.py` to log data to desired sink before, during, and after any data transformations.
Start by importing the `azure-ai-mlmonitoring` package in `score.py`
```
import pandas as pd
import json
from azure.ai.mlmonitoring import Collector
def init():
global inputs_collector, outputs_collector
# instantiate collectors with appropriate names, make sure align with deployment spec
inputs_collector = Collector(name='model_inputs')
outputs_collector = Collector(name='model_outputs')
def run(data):
# convert json to python object and convert to pandas Dataframe
input_df = pd.DataFrame(json.loads(data))
# collect inputs data, store correlation_context
context = inputs_collector.collect(input_df)
# perform scoring with pandas Dataframe, return value is also pandas Dataframe
output_df = predict(input_df)
# collect outputs data, pass in correlation_context so inputs and outputs data can be correlated later
outputs_collector.collect(output_df, context)
return output_df.to_dict()
def predict(input_df):
# process input and return with outputs
...
return output_df
```
Create environment with base image `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04` and conda dependencies, then build the environment.
```
channels:
- conda-forge
dependencies:
- python=3.8
- numpy=1.23.5
- pandas=1.5.2
- pip=22.3.1
- pip:
- azureml-defaults==1.38.0
- requests==2.28.1
- azure-ai-mlmonitoring
name: model-env
```
Create deployment with custom logging enabled (model_inputs and model_outputs are enabled) and the environment you just built, please update the yaml according to your scenario.
```
#source ../configs/model-data-collector/data-storage-basic-OnlineDeployment.YAML
$schema: http://azureml/sdk-2-0/OnlineDeployment.json
endpoint_name: my_endpoint #unchanged
name: blue #unchanged
model: azureml:my-model-m1:1 #azureml:models/<name>:<version> #unchanged
environment: azureml:custom-logging-env:1 #unchanged
data_collector:
model_inputs:
enabled: true
model_outputs:
enabled: true
```
# Change Log
## [v0.1.0a2](https://pypi.org/project/azure-ai-mlmonitoring/0.1.0a2/) (2023.1.16)
**Improvements**
- Refine README.md.
## [v0.1.0a1](https://pypi.org/project/azure-ai-mlmonitoring/0.1.0a1/) (2023.1.4)
**New Features**
- Support model data collection for pandas Dataframe.
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"description": "# Azure Machine Learning Model Monitoring SDK\n\nThe `azure-ai-mlmonitoring` package provides an SDK to enable Model Data Collector (MDC) for custom logging allows customers to collect data at arbitrary points in their data pre-processing pipeline. Customers can leverage SDK in `score.py` to log data to desired sink before, during, and after any data transformations. \n\nStart by importing the `azure-ai-mlmonitoring` package in `score.py`\n\n```\nimport pandas as pd\nimport json\nfrom azure.ai.mlmonitoring import Collector\n\ndef init():\n global inputs_collector, outputs_collector\n\n # instantiate collectors with appropriate names, make sure align with deployment spec\n inputs_collector = Collector(name='model_inputs') \n outputs_collector = Collector(name='model_outputs')\n\ndef run(data): \n # convert json to python object and convert to pandas Dataframe\n input_df = pd.DataFrame(json.loads(data))\n\n # collect inputs data, store correlation_context\n context = inputs_collector.collect(input_df) \n\n # perform scoring with pandas Dataframe, return value is also pandas Dataframe\n output_df = predict(input_df) \n\n # collect outputs data, pass in correlation_context so inputs and outputs data can be correlated later\n outputs_collector.collect(output_df, context)\n \n return output_df.to_dict()\n \ndef predict(input_df):\n # process input and return with outputs\n ...\n \n return output_df\n```\n\nCreate environment with base image `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04` and conda dependencies, then build the environment.\n\n```\nchannels:\n - conda-forge\ndependencies:\n - python=3.8\n - numpy=1.23.5\n - pandas=1.5.2\n - pip=22.3.1\n - pip:\n - azureml-defaults==1.38.0\n - requests==2.28.1\n - azure-ai-mlmonitoring\nname: model-env\n```\n\nCreate deployment with custom logging enabled (model_inputs and model_outputs are enabled) and the environment you just built, please update the yaml according to your scenario.\n\n```\n#source ../configs/model-data-collector/data-storage-basic-OnlineDeployment.YAML\n$schema: http://azureml/sdk-2-0/OnlineDeployment.json\n\nendpoint_name: my_endpoint #unchanged\nname: blue #unchanged\nmodel: azureml:my-model-m1:1 #azureml:models/<name>:<version> #unchanged\nenvironment: azureml:custom-logging-env:1 #unchanged\ndata_collector:\n model_inputs:\n enabled: true\n model_outputs:\n enabled: true\n```\n\n# Change Log\n\n## [v0.1.0a2](https://pypi.org/project/azure-ai-mlmonitoring/0.1.0a2/) (2023.1.16)\n\n**Improvements**\n\n- Refine README.md.\n\n## [v0.1.0a1](https://pypi.org/project/azure-ai-mlmonitoring/0.1.0a1/) (2023.1.4)\n\n**New Features**\n\n- Support model data collection for pandas Dataframe.\n",
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