Why is continuous learning important in deploying machine learning models with PSE Cortex?

Continuous learning is crucial in deploying machine learning models with PSE Cortex because it enables models to adapt to new data and improve predictions over time. As data distributions can shift due to various factors, such as changes in user behavior, market dynamics, or external influences, models can become less effective if they are not updated with new information. By incorporating continuous learning, the model can adjust its parameters and learn from fresh data inputs, ensuring that it remains relevant and accurate in its predictions.

This adaptive learning process helps maintain the model's performance, reduces the risk of obsolescence, and ultimately enhances decision-making processes in real-time applications. Continuous learning supports ongoing enhancement, allowing businesses to leverage the most current insights from their data, leading to better outcomes and informed strategies.