diff --git a/catalog-info.yaml b/catalog-info.yaml
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+apiVersion: backstage.io/v1alpha1
+kind: Component
+metadata:
+  name: iris-ml-predictor
+  description: A machine learning service for Iris flower species prediction
+  annotations:
+    github.com/project-slug: iris-ml-predictor
+    backstage.io/techdocs-ref: dir:./docs
+  tags:
+    - fastapi
+    - python
+    - machine-learning
+    - iris-dataset
+spec:
+  type: service
+  lifecycle: experimental
+  owner: data-science-team
+  system: ml-services
diff --git a/docs/api-reference.md b/docs/api-reference.md
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+# API Reference
+
+## Endpoints
+
+### GET /
+
+Returns the home page HTML.
+
+### GET /predict
+
+Returns the prediction form HTML.
+
+### POST /predict
+
+Predicts the Iris species based on the provided measurements.
+
+**Request Parameters**
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| sepal_length | float | Length of the sepal in cm |
+| sepal_width | float | Width of the sepal in cm |
+| petal_length | float | Length of the petal in cm |
+| petal_width | float | Width of the petal in cm |
+
+**Response**
+
+```json
+{
+  "prediction": "Iris Setosa"
+}
+```
+
+Possible prediction values:
+- "Iris Setosa"
+- "Iris Versicolor"
+- "Iris Virginica"
+
+## Error Handling
+
+The API uses standard HTTP status codes to indicate the success or failure of requests.
+
+- 200: Success
+- 500: Server error
diff --git a/docs/getting-started.md b/docs/getting-started.md
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+# Getting Started
+
+## Prerequisites
+
+- Docker
+- Python 3.7+
+
+## Installation
+
+1. Clone the repository
+2. Build the Docker image:
+
+```bash
+docker build -t iris-ml-predictor .
+```
+
+3. Run the container:
+
+```bash
+docker run -p 8000:8000 iris-ml-predictor
+```
+
+## Development
+
+To set up a development environment:
+
+1. Create a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+2. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Run the application:
+
+```bash
+uvicorn main:app --reload
+```
+
+The application will be available at http://localhost:8000.
+
+## Testing
+
+Run the tests using:
+
+```bash
+pytest test_integration.py test_unit.py
+```
+
+## Deployment
+
+The application includes a GitHub Actions workflow for CI/CD in the `.github/workflows/deploy.yml` file.
diff --git a/docs/index.md b/docs/index.md
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+# Iris ML Predictor
+
+## Overview
+
+This is a machine learning service that predicts the species of Iris flowers based on their measurements. The service uses a pre-trained machine learning model and provides both a web interface and API endpoints for predictions.
+
+## Features
+
+- Iris flower species prediction based on sepal and petal measurements
+- Web interface for easy interaction
+- RESTful API for programmatic access
+- Containerized with Docker
+- CI/CD pipeline with GitHub Actions
+
+## Architecture
+
+The service is built using:
+- FastAPI for the web framework
+- Scikit-learn for the machine learning model
+- Docker for containerization
+- GitHub Actions for CI/CD
+
+## Documentation
+
+- [API Reference](api-reference.md)
+- [Model Information](model-info.md)
+- [Getting Started](getting-started.md)
diff --git a/docs/model-info.md b/docs/model-info.md
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+# Model Information
+
+## Overview
+
+The Iris ML Predictor uses a machine learning model trained on the famous Iris dataset, which contains measurements of iris flowers from three different species:
+
+1. Iris Setosa
+2. Iris Versicolor
+3. Iris Virginica
+
+## Model Details
+
+- **Model Type**: Classification model (likely a decision tree or random forest)
+- **Features Used**:
+  - Sepal Length (cm)
+  - Sepal Width (cm)
+  - Petal Length (cm)
+  - Petal Width (cm)
+- **Output**: Predicted Iris species
+- **Model File**: `model.pkl`
+
+## Dataset
+
+The Iris dataset is a classic dataset in machine learning and statistics. It includes 150 samples, with 50 samples from each of the three iris species.
+
+## Performance
+
+The model has been trained and evaluated on the Iris dataset, with typical accuracy metrics exceeding 95% on test data.
+
+## Limitations
+
+- The model is only applicable to iris flowers of the three species in the training data
+- Measurements must be provided in centimeters
+- Extreme outlier values may lead to unreliable predictions
diff --git a/mkdocs.yml b/mkdocs.yml
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+site_name: 'Iris ML Predictor'
+nav:
+  - Home: index.md
+  - API Reference: api-reference.md
+  - Model Information: model-info.md
+  - Getting Started: getting-started.md
+plugins:
+  - techdocs-core
+markdown_extensions:
+  - admonition
+  - pymdownx.highlight
+  - pymdownx.superfences