How should you configure an MLOps pipeline to ensure that the model is trained and deployed with the most recent data?

To ensure that the model in an MLOps pipeline is trained and deployed with the most recent data, utilizing event-driven triggers is crucial. This approach allows the pipeline to automatically initiate processing as soon as new data becomes available. It is an efficient method that not only minimizes delays in model updates but also optimizes resource usage, as the pipeline only runs when necessary.

This configuration enables rapid adaptation to new information, ensuring that the model's performance reflects the latest trends and patterns in the data. Furthermore, it reduces the likelihood of running redundant jobs when no new data is present, thereby saving computational resources and time.

In contrast, scheduling jobs to run at fixed intervals, such as every hour, does not take into account whether any new data has been generated. This can lead to wasted resources and potential staleness of the model if significant changes in the data landscape occur between scheduled runs. Similarly, running jobs during off-peak hours without considering data freshness overlooks the primary goal of keeping the model updated, focusing instead only on resource optimization. Lastly, running the entire pipeline manually whenever new data is available adds unnecessary overhead and risks potential updates being delayed or overlooked entirely. Event-driven triggers streamline this process by automating the response to data changes.