Develop a lightweight Python client using App Engine to continuously monitor Cloud Storage for new files and initiate the training job upon detection, ensuring minimal latency.

Configure Cloud Scheduler to periodically check the Cloud Storage bucket for new files. If no new files are found since the last check, the job is terminated to save resources.

Implement a Cloud Storage trigger to send a notification to a Pub/Sub topic when a new file is added. Use a Pub/Sub-triggered Cloud Function to start the training job on GKE, leveraging event-driven architecture for efficiency.

Utilize Dataflow to process and store files in Cloud Storage, then automatically trigger the training job on GKE once the file is saved, ensuring data is processed in a streamlined manner.

Combine both Cloud Scheduler for periodic checks and Cloud Storage triggers for immediate notifications, creating a hybrid approach that ensures no new data is missed while optimizing resource usage.