What are the best ways to incorporate continuous learning in your ML workflow?
Continuous learning is the ability of a machine learning (ML) model to adapt to new data and feedback over time, without requiring a complete retraining or redeployment. This is essential for ML applications that operate in dynamic and changing environments, such as online recommendation systems, fraud detection, natural language processing, and computer vision. In this article, you will learn some of the best ways to incorporate continuous learning in your ML workflow, from data collection and preprocessing, to model design and evaluation, to deployment and monitoring.