The choice of models and strategies relies entirely on the nature of your data. Several machine learning models update their parameters as the data evolves, handling the dynamic scenario.

If your focus lies on addressing the adaptability to changes in problem settings and scalability, explore Transfer Learning. For cases that demand sequential decision-making, consider the state-of-the-art Deep Reinforcement Learning methods.

Online machine learning in dynamic datasets presents unique challenges such as concept drift, varying data distributions, and evolving patterns over time. Several innovative strategies have been developed to address these challenges and make online learning more adaptive, accurate, and resource-efficient. Here are some strategies:

When implementing these strategies, it's essential to consider the specific characteristics of the dynamic dataset, the nature of the learning task, and the available computational resources. Continuous monitoring and evaluation are critical to ensure that the online learning system maintains accuracy and adapts effectively to changes in the data distribution.