Can you tell about the system architecture chosen, where machine learning was used in the project implementation?

In one of my projects involving machine learning, I used a microservices architecture where each service was responsible for a specific stage of data processing and model training.

Main components of the architecture:

• Data collection and preprocessing service: collected data from various sources, cleaned and normalized it.
• Model training service: trained the model on prepared data using libraries like TensorFlow or scikit-learn.
• Inference service: provided an API for obtaining predictions from the trained model.
• Monitoring and logging: tracked model quality and service performance.

This approach allowed for independent scaling of each component, quick model updates, and integration of ML into the overall system.

An example of a simplified diagram:

[Data Collection] -> [Preprocessing] -> [Model Training] -> [Inference API]

For implementation, Docker containers and Kubernetes orchestration were used, ensuring reliability and flexibility in deployment.