Continual learning is a machine learning approach that allows models to incrementally learn from data streams without accessing past data, addressing challenges such as changing data distributions and the need for model personalization. It involves regularization-based, architectural, and memory-based methods, each with its own pros and cons. Continual learning is crucial for environments where models must adapt quickly to new information, such as fraud detection or personalized document classification. The field faces challenges like "catastrophic forgetting," where models forget previous knowledge when learning new information. Memory-based methods are particularly effective but require access to past data, which isn't always feasible due to privacy or storage constraints. Implementing continual learning is a gradual process, often starting with traditional model training and evolving through stages of automation and incremental learning. Choosing the right approach depends on the specific use case, and while regularization methods are easy to implement, memory-based approaches tend to be more effective. The adoption of continual learning is essential for large-scale operations where manually retraining models is impractical, and the journey toward effective continual learning involves careful planning and experimentation.