To architect an AI-based image recognition system using Python, I would start by defining the project requirements and understanding the specific use cases. This involves collaborating with stakeholders to gather detailed requirements and translating them into technical specifications. 

Next, I would choose an appropriate machine learning framework, such as TensorFlow or PyTorch, which are well-suited for image recognition tasks. I would then design the system architecture, which includes data collection, preprocessing, model training, and deployment. For data collection, I would ensure we have a diverse and representative dataset, followed by preprocessing steps like resizing, normalization, and augmentation to improve model performance.

For the model training phase, I would select a suitable neural network architecture, such as a Convolutional Neural Network (CNN), which is effective for image recognition tasks. I would then train the model using the prepared dataset, ensuring to split the data into training, validation, and test sets to evaluate the model's performance accurately.

In terms of design patterns, I would apply the Model-View-Controller (MVC) pattern to separate the data processing, user interface, and control logic, ensuring a modular and maintainable codebase. Additionally, I would use the Repository pattern to manage data access, making it easier to switch between different data sources if needed.

Finally, I would focus on deploying the model using a cloud platform like AWS or Azure, which provides scalability and reliability. I would also implement continuous integration and deployment (CI/CD) pipelines to automate testing and deployment processes, ensuring the system remains robust and up-to-date with the latest improvements. Throughout the project, I would conduct regular code reviews and testing to maintain high-quality standards and address any issues promptly.