doc(monitoring): monitoring guide

docs/source/guides/index.rst

@@ -20,6 +20,7 @@ into this part of the documentation.
     graph
     logging
     metrics
+    monitoring
     performance
     grpc
     gpu

docs/source/guides/monitoring.rst

@@ -0,0 +1,237 @@
+============================================
+Inference Data Collection & Model Monitoring
+============================================
+
+Data-Centric Artificial Intelligence is a popular paradigm that is
+revolutionizing the way we build AI systems. It is a new way of thinking
+about AI that is based on the idea that data is the most important
+component of AI systems.
+
+BentoML embraces this new paradigm by providing APIs that make a data-centric workflow easy to implement.
+
+.. image:: ../_static/img/monitoring_workflow.png
+   :width: 600px
+   :align: center
+
+In this guide, we will focus on the online data collection and model monitoring. BentoML provides a unified interface for that.
+
+The benefits of having a data collection and model monitoring workflow includes:
+- Monitoring key statistical business metrics
+- Identify data drift early to help decide if the model needs to be retrained
+- make QA possible for the previous untracked metrics, like data and model performance
+- use the same data collection and model monitoring workflow for training or model iteration
+
+
+Build an ML Application with monitoring API
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following examples are excerpted from :github:`bentoml/BentoML/tree/main/examples/quickstart`.
+
+Given that we have the definetion of the bento service:
+
+:code:`service.py`
+
+.. code:: python
+    :caption: `service.py`
+
+    import numpy as np
+    import bentoml
+    from bentoml.io import Text
+    from bentoml.io import NumpyNdarray
+
+    CLASS_NAMES = ["setosa", "versicolor", "virginica"]
+
+    iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()
+
+    svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])
+
+    @svc.api(
+        input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)),
+        output=Text(),
+    )
+    async def classify(features: np.ndarray) -> str:
+        results = await iris_clf_runner.predict.async_run([features])
+        result = results[0]
+        category = CLASS_NAMES[result]
+        return category
+
+Before we go to the production, we will only need one more step to add monitoring:
+
+
+.. code:: python
+    :caption: `service.py`
+
+    import numpy as np
+
+    import bentoml
+    from bentoml.io import Text
+    from bentoml.io import NumpyNdarray
+
+    CLASS_NAMES = ["setosa", "versicolor", "virginica"]
+
+    iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()
+    svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])
+
+    @svc.api(
+        input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)),
+        output=Text(),
+    )
+    async def classify(features: np.ndarray) -> str:
+        with bentoml.monitor("iris_classifier_prediction") as mon:
+            mon.log(features[0], name="sepal length", role="feature", data_type="numerical")
+            mon.log(features[1], name="sepal width", role="feature", data_type="numerical")
+            mon.log(features[2], name="petal length", role="feature", data_type="numerical")
+            mon.log(features[3], name="petal width", role="feature", data_type="numerical")
+
+            results = await iris_clf_runner.predict.async_run([features])
+            result = results[0]
+            category = CLASS_NAMES[result]
+
+            mon.log(category, name="pred", role="prediction", data_type="categorical")
+        return category
+
+
+Here we logged the request features and predictions with the `bentoml.monitor` API. Here’s the [API reference].
+
+Then we can continue the normal steps, build a bento (BentoML Application) for the above service.
+
+.. code:: bash
+
+    $ bentoml build
+    ██████╗░███████╗███╗░░██╗████████╗░█████╗░███╗░░░███╗██╗░░░░░
+    ██╔══██╗██╔════╝████╗░██║╚══██╔══╝██╔══██╗████╗░████║██║░░░░░
+    ██████╦╝█████╗░░██╔██╗██║░░░██║░░░██║░░██║██╔████╔██║██║░░░░░
+    ██╔══██╗██╔══╝░░██║╚████║░░░██║░░░██║░░██║██║╚██╔╝██║██║░░░░░
+    ██████╦╝███████╗██║░╚███║░░░██║░░░╚█████╔╝██║░╚═╝░██║███████╗
+    ╚═════╝░╚══════╝╚═╝░░╚══╝░░░╚═╝░░░░╚════╝░╚═╝░░░░░╚═╝╚══════╝
+
+    Successfully built Bento(tag="iris_classifier:6aqnksdbuouf2usu").
+
+Deploy the service and collect monitoring data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+With BentoML, once we have the bento, it's easy to deploy the ML application to any target. https://docs.bentoml.org/en/latest/concepts/deploy.html
+
+Use ``serve --production`` to start the bento in production mode as a standalone server:
+
+.. code:: bash
+
+    $ bentoml serve iris_classifier --production
+
+Then we can send a request to the server to get the prediction. BentoML will log the request features and predictions to the configured place.
+
+By default BentoML will export the data to the `monitoring/<your_monitor_name>` directory. To preview:
+
+.. code:: bash
+
+    $ tail -f monitoring/iris_classifier_prediction/data/*.log
+    ==> monitoring/iris_classifier_prediction/data/data.1.log <==
+    {"sepal length": 6.3, "sepal width": 2.3, "petal length": 4.4, "petal width": 1.3, "pred": "versicolor", "timestamp": "2022-11-09T15:31:26.781914", "request_id": "10655923893485958044"}
+    {"sepal length": 4.9, "sepal width": 3.6, "petal length": 1.4, "petal width": 0.1, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.786670", "request_id": "16263733333988780524"}
+    {"sepal length": 7.7, "sepal width": 3.0, "petal length": 6.1, "petal width": 2.3, "pred": "virginica", "timestamp": "2022-11-09T15:31:26.788535", "request_id": "9077185615468445403"}
+    {"sepal length": 7.4, "sepal width": 2.8, "petal length": 6.1, "petal width": 1.9, "pred": "virginica", "timestamp": "2022-11-09T15:31:26.795290", "request_id": "1949956912055125154"}
+    {"sepal length": 5.0, "sepal width": 2.3, "petal length": 3.3, "petal width": 1.0, "pred": "versicolor", "timestamp": "2022-11-09T15:31:26.797957", "request_id": "5892192931675972870"}
+    {"sepal length": 5.1, "sepal width": 3.5, "petal length": 1.4, "petal width": 0.3, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.801006", "request_id": "11124174524929195678"}
+    {"sepal length": 5.4, "sepal width": 3.4, "petal length": 1.7, "petal width": 0.2, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.805018", "request_id": "1977947867380701804"}
+    {"sepal length": 5.4, "sepal width": 3.0, "petal length": 4.5, "petal width": 1.5, "pred": "versicolor", "timestamp": "2022-11-09T15:31:26.809391", "request_id": "5170522495321543267"}
+    {"sepal length": 6.5, "sepal width": 3.2, "petal length": 5.1, "petal width": 2.0, "pred": "virginica", "timestamp": "2022-11-09T15:31:26.813963", "request_id": "746111233619919779"}
+    {"sepal length": 5.4, "sepal width": 3.7, "petal length": 1.5, "petal width": 0.2, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.816515", "request_id": "10451493838968794158"}
+
+    ==> monitoring/iris_classifier_prediction/data/data.2.log <==
+    {"sepal length": 5.6, "sepal width": 2.5, "petal length": 3.9, "petal width": 1.1, "pred": "versicolor", "timestamp": "2022-11-09T15:31:26.768545", "request_id": "12581333339958540887"}
+    {"sepal length": 4.5, "sepal width": 2.3, "petal length": 1.3, "petal width": 0.3, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.770188", "request_id": "14803218836235991321"}
+    {"sepal length": 6.3, "sepal width": 2.9, "petal length": 5.6, "petal width": 1.8, "pred": "virginica", "timestamp": "2022-11-09T15:31:26.771554", "request_id": "3898998431725264845"}
+    {"sepal length": 4.7, "sepal width": 3.2, "petal length": 1.6, "petal width": 0.2, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.775306", "request_id": "16171654492399963820"}
+    {"sepal length": 4.9, "sepal width": 3.0, "petal length": 1.4, "petal width": 0.2, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.778971", "request_id": "12433921846139166785"}
+    {"sepal length": 6.9, "sepal width": 3.1, "petal length": 5.4, "petal width": 2.1, "pred": "virginica", "timestamp": "2022-11-09T15:31:26.783441", "request_id": "3868728687839356795"}
+    {"sepal length": 5.1, "sepal width": 3.4, "petal length": 1.5, "petal width": 0.2, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.803871", "request_id": "4920762203256166127"}
+    {"sepal length": 4.5, "sepal width": 2.3, "petal length": 1.3, "petal width": 0.3, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.807770", "request_id": "562712759995883379"}
+    {"sepal length": 5.1, "sepal width": 3.8, "petal length": 1.6, "petal width": 0.2, "pred": "setosa", "timestamp": "2022-11-09T15:31:26.810136", "request_id": "15755243536090754018"}
+    {"sepal length": 6.4, "sepal width": 3.1, "petal length": 5.5, "petal width": 1.8, "pred": "virginica", "timestamp": "2022-11-09T15:31:26.812188", "request_id": "15915060852312696387"}
+
+
+Shipping the collected data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+BentoML has a general monitoring data collecting API. This makes it possible to ship collected data to anywhere without code changes.
+For example to a data warehouse, data analyze pipelines or to a monitoring & drift detection solution.
+
+To achieve this, we just neet to provide a deployment configuration to bentoml.
+
+Built-in Monitoring Data Collectors
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1. Through log files
+
+The most common way to collect monitoring data is to write it to log files. Many utils like fluentbit, filebeat, logstash, etc. can be used to collect log files and ship them to a data warehouse or a monitoring system.
+This is also the default way BentoML exports monitoring data:
+
+.. code:: yaml
+    :caption: `deployment_configuration.yaml`
+
+    monitoring:
+      enabled: true
+      type: default
+      options:
+        log_path: path/to/log/file
+
+For Docker deployments, user can mount the log directory to a volume to persist the log files.
+For K8s deployments, user can mount the log directory, and deploy a fluentbit daemonset or sidecar container to collect the log files to target destinations.
+
+
+2. Through a OTLP endpoint
+
+.. code:: yaml
+    :caption: `deployment_configuration.yaml`
+    monitoring:
+      enable: true
+      type: otlp
+      options:
+        endpoint: http://localhost:5000
+        insecure: true
+        credentials: null
+        headers: null
+        timeout: 10
+        compression: null
+        meta_sample_rate: 1.0,
+
+For some deployment platforms, it's not easy to collect log files. For example, AWS Lambda doesn't support log files. In this case, BentoML can export monitoring data to an OTLP endpoint.
+Some log collectors like fluentbit also supports OTLP input.
+
+
+Plugins and Third-party Monitoring Data Collectors
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+BentoML also supports plugins and third-party monitoring data collectors. User can write a custom monitoring data collector and publish it as a python package.
+Unlike built-ins are more protocol specific for general use cases,
+plugins could be more platform specific.
+
+.. note::
+    To use plugins, you need to install the plugin and include it in the dependencies section of the bentofile.
+    For example, it is required to add `bentoml-plugins-arize` to the `python:packages` to use the Arize plugin.
+
+1. Ship to Arize AI
+
+For end-to-end solutions for data/model monitoring, BentoML colaborates with Arize AI to provide a plugin for Arize.
+If you don't want to deploy a pipeline by yourself but still need data and model monitoring for the bussiness, Arize AI is a good choice.
+
+Arize AI provides a unified platform for data scientists, data engineers, and ML engineers to monitor, analyze, and debug ML models in production.
+And the `bentoml-plugins-arize` makes it easy to work with BentoML.
+
+
+.. code:: yaml
+    :caption: `deployment_configuration.yaml`
+
+    monitoring:
+        enable: true
+        type: bentoml_plugins.arize.ArizeMonitor
+        options:
+            space_key: <your_space_key>
+            api_key: <your_api_key>
+            # ... more arize options
+            # see https://docs.arize.com/arize/data-ingestion/api-reference/python-sdk/arize.init#keyword-arguments
+            # and https://docs.arize.com/arize/sending-data-to-arize/data-ingestion-methods/sdk-reference/python-sdk/arize.log
+
+
+
+You could also join the BentoML community and develop your own monitoring data collector by implementing the `bentoml.monitoring.MonitorBase` interface.

docs/source/integrations/arize.rst

@@ -0,0 +1,41 @@
+========
+Arize AI
+========
+
+Arize AI provides a unified platform for data scientists, data engineers, and ML engineers to monitor, analyze, and debug ML models in production.
+If you don't want to deploy a pipeline by yourself but still need data and model monitoring for the bussiness, Arize AI is a good choice.
+
+For end-to-end solutions for data/model monitoring, BentoML colaborates with Arize AI to make it easy to work with all BentoML Deployments.
+
+
+.. seealso::
+   How to use:
+   :ref:`Inference Data Collection & Model Monitoring <guides/monitoring:Plugins and Third-party Monitoring Data Collectors>`.
+
+
+Preview
+~~~~~~~
+
+* Drift Detection & Monitoring
+
+.. image:: ../_static/img/arize_drift_monitoring.png
+   :width: 100%
+   :alt: Arize Drift Monitoring
+
+* Data Quality Monitoring
+
+.. image:: ../_static/img/arize_data_quality_monitoring.png
+   :width: 100%
+   :alt: Arize Data Quality Monitoring
+
+* Model Explainability
+
+.. image:: ../_static/img/arize_model_explainability.png
+   :width: 100%
+   :alt: Arize Model Explainability
+
+* Alerting & Notification
+
+.. image:: ../_static/img/arize_alerting.png
+   :width: 100%
+   :alt: Arize Alerting

docs/source/integrations/index.rst

@@ -10,3 +10,4 @@ Integrations
     airflow
     flink
     mlflow
+    arize