diff --git a/docs/how-to-guides/.pages b/docs/how-to-guides/.pages
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 nav:
   - index.md
   - Integrating Autoware: integrating-autoware
+  - Training Machine Learning Models: training-machine-learning-models
   - others
diff --git a/docs/how-to-guides/index.md b/docs/how-to-guides/index.md
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 - [Overview](integrating-autoware/overview.md)
 
+## Training machine learning models
+
+- [Model Training and Deploying](training-machine-learning-models/training-models.md)
+
 ## Others
 
 - [Debug Autoware](others/debug-autoware.md)
diff --git a/docs/how-to-guides/training-machine-learning-models/.pages b/docs/how-to-guides/training-machine-learning-models/.pages
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+nav:
+  - training-models.md
diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md
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+# Training Machine Learning Models
+
+## Overview
+
+The Autoware offers a comprehensive array of machine learning models, tailored for a wide range of tasks including 2D and 3D object detection,
+traffic light recognition and more. These models have been meticulously trained utilizing **[open-mmlab](https://github.com/open-mmlab)**'s extensive repositories.
+By leveraging the provided scripts and following the training steps, you have the capability to train these models using your own dataset,
+tailoring them to your specific needs.
+
+Furthermore, you will find the essential conversion scripts to deploy your trained models into Autoware using the **[mmdeploy](https://github.com/open-mmlab/mmdeploy)** repository.
+
+## Training traffic light classifier model
+
+The traffic light classifier model within the Autoware has been trained using the **[mmlab/pretrained](https://github.com/open-mmlab/mmpretrain)** repository.
+The Autoware offers pretrained models based on EfficientNet-b1 and MobileNet-v2 architectures.
+To fine-tune these models, a total of 83,400 images were employed, comprising 58,600 for training,
+14,800 for evaluation, and 10,000 for testing. These images represent Japanese traffic lights and were trained using TIER IV's internal dataset.
+
+| Name            | Input Size | Test Accuracy |
+| --------------- | ---------- | ------------- |
+| EfficientNet-b1 | 128 x 128  | 99.76%        |
+| MobileNet-v2    | 224 x 224  | 99.81%        |
+
+Comprehensive training instructions for the traffic light classifier model are detailed within
+the readme file accompanying **"traffic_light_classifier"** package. These instructions will guide you through
+the process of training the model using your own dataset. To facilitate your training, we have also provided
+an example dataset containing three distinct classes (green, yellow, red), which you can leverage during the training process.
+
+Detailed instructions for training the traffic light classifier model can be found **[here](https://github.com/autowarefoundation/autoware.universe/blob/main/perception/traffic_light_classifier/README.md)**.
+
+<!--
+
+Training traffic light detection model and lidar CenterPoint model will be added there.
+
+-->