In the realm of machine learning, deploying a model is just the beginning of its lifecycle, as continuous monitoring, retraining, and adaptation are crucial to maintaining model performance due to changes in real-world data. This process, known as MLOps, involves setting up systems for model serving, performance monitoring, and retraining to counter data drift and concept drift, which occur when the statistical properties of data or target variables evolve over time. Key strategies for retraining include periodic retraining, performance-based triggers, and responding to data changes, with approaches ranging from offline or batch learning to online or incremental learning. Tools like neptune.ai and Qualdo facilitate continuous monitoring and experimentation, ensuring that models remain up-to-date and adaptive to new data patterns. Effective continuous training and monitoring practices involve balancing computational and labor costs while leveraging machine learning principles for automation, reusability, reproducibility, and manageability.