Intermediate · Systems
Feedback loop
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A feedback loop occurs when a model's output influences its future input or training data, potentially leading to system drift or reinforcement of biases.
Technical Definition
A feedback loop occurs when a model's output influences its future input or training data, potentially leading to system drift or reinforcement of biases.
How it works
A feedback loop in machine learning describes a scenario where a model's predictions or actions affect the data it will process or learn from in the future. This can create a self-reinforcing cycle, where the model's behavior can degrade or improve over time depending on the nature of the loop. Monitoring and managing these loops are essential in production systems.
Related Concepts
- Data Drift — When the statistical distribution of inputs to a deployed model changes over time.
- Model Deployment — The process of making a trained machine learning model available for use in a production environment.