Chronos: AIOps User Guide (#7960)

* add aiops doc

* add image

* fix typo

docs/readthedocs/source/_toc.yml

@@ -238,6 +238,7 @@ subtrees:
                 - file: doc/Chronos/Overview/forecasting
                 - file: doc/Chronos/Overview/anomaly_detection
                 - file: doc/Chronos/Overview/simulation
+                - file: doc/Chronos/Overview/aiops
                 - file: doc/Chronos/Overview/speed_up
                 - file: doc/Chronos/Overview/useful_functionalities
           - file: doc/Chronos/Howto/index

docs/readthedocs/source/doc/Chronos/Overview/aiops.md

@@ -0,0 +1,87 @@
+# Artificial Intelligence for IT operations (AIOps)
+
+Chronos provides a template(i.e., `ConfigGenerator`) as an easy-to-use builder for an AIOps decision system with the usage of `Trigger`.
+
+## How does it work
+
+AIOps application typically relies on a decision system with one or multiple AI models. Generally, this AI system needs to be trained with some training data with a self-defined checkpoint. When using the AI system, we first initialize it throught previously trained checkpoint and inform the AI system with current status to get the suggested configuration.
+
+![](../Image/aiops-workflow.png)
+
+Sometimes the AI system need to be informed some **timely** information (e.g., some events in log or some monitoring data every second). Chronos also defines some triggers for this kind of usage.
+
+## Define ConfigGenerator
+
+### Start from a trivial ConfigGenerator
+Chronos provides `bigdl.chronos.aiops.ConfigGenerator` as a template for users to define their own AIOps AI system. Following is a "hello-world" case.
+
+```python
+class MyConfigGenerator(ConfigGenerator):
+    def __init__(self):
+        super().__init__()
+        self.best_config = [3.0, 1.6]
+
+    def genConfig(self):
+        return self.best_config
+```
+
+For this self-defined `MyConfigGenerator`, we keep generate a fixed best config with out considering current status. This could be a startpoint or smoke test configgenerator for your system. The whole system even do not need to be trained.
+
+### Add AI Model to ConfigGenerator
+Any model could be used in `ConfigGenerator`, to name a few, sklearn, pytorch or tensorflow models are all valid. Following is a normal flow you may want to add your model.
+
+```python
+class MyConfigGenerator(ConfigGenerator):
+    def __init__(self, path):
+        super().__init__()
+        self.model = load_model_from_checkpoint(path)
+
+    def genConfig(self, current_status):
+        return self.model(current_status)
+
+    @staticmethod
+    def train(train_data, path):
+        train_model_and_save_checkpoint(train_data, path)
+```
+
+- In `MyConfigGenerator.train`, users will define the way to train their model and save to a specific path.
+- In `MyConfigGenerator.__init__`, users will define the way to load the trained checkpoint.
+- In `MyConfigGenerator.genConfig`, users will define the way to use the loaded model to do the prediction and get the suggested config.
+
+Please refer to [ConfigGenerator API doc](../../PythonAPI/Chronos/aiops.html) for detailed information.
+
+#### Use Chronos Forecaster/Anomaly detector
+Chronos also provides some out-of-box forecasters and anomaly detectors for time series data for users to build their AIOps use-case easier.
+
+Please refer to [Forecaster User Guide](./forecasting.html) and [Anomaly Detector User Guide](./anomaly_detection.html) for detailed information.
+
+### Use trigger in ConfigGenerator
+Sometimes the AI system need to be informed some **timely** information (e.g., some events in log or some monitoring data every second). Chronos also defines some triggers for this kind of usage. Following is a trivial case to help users understand what a `Trigger` can do. 
+
+```python
+class MyConfigGenerator(ConfigGenerator):
+    def __init__(self):
+        self.sweetpoint = 1
+        super().__init__()
+
+    def genConfig(self):
+        return self.sweetpoint
+
+    @triggerbyclock(2)
+    def update_sweetpoint(self):
+        self.sweetpoint += 1
+```
+
+In this case, once the `MyConfigGenerator` is initialized, `update_sweetpoint` will be called every 2 seconds, users will thus get an evolving ConfiguGenerator.
+
+```python
+mycg = MyConfigGenerator(1)
+time.sleep(2)
+assert mycg.genConfig() == 2
+time.sleep(2)
+assert mycg.genConfig() == 3
+```
+
+This trivial case may seem useless, but with a dedicated `update_sweetpoint`, such as get the CPU utils every second, users could bring useful information to their ConfigGenerator and make better decision with easy programming.
+
+Please refer to [Trigger API doc](../../PythonAPI/Chronos/aiops.html) for detailed information.

docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst

@@ -5,5 +5,6 @@ Chronos Deep Dive
 * `Time Series Forecasting <forecasting.html>`__ introduces how to build a time series forecasting application.
 * `Time Series Anomaly Detection <anomaly_detection.html>`__ introduces how to build a anomaly detection application.
 * `Generate Synthetic Sequential Data <simulation.html>`__ introduces how to build a series data generation application.
+* `Artificial Intelligence for IT operations (AIOps)`__ introduces how to build an AI system for AIOps use-cases.
 * `Speed up Chronos built-in/customized models <speed_up.html>`__ introduces how to speed up chronos built-in models/customized time-series models
 * `Useful Functionalities <useful_functionalities.html>`__ introduces some functionalities provided by Chronos that can help you improve accuracy/performance or scale the application to a larger data.