Deploying a Google Kubernetes Engine cluster with a V100 GPU Node Pool and an NFS Server for data storage, which offers flexibility but requires manual management and incurs higher costs.

Utilizing a Deep Learning VM instance with 4 V100 GPUs and local storage, which provides high performance but lacks scalability and cost-efficiency for daily retraining.

Opting for an AI Platform Training job with a custom scale tier specifying 4 V100 GPUs and Cloud Storage, which offers a fully managed, scalable, and cost-efficient solution for model training.

Choosing a Deep Learning VM with 4 V100 GPUs and Cloud Storage, which improves data storage scalability but still involves VM management overhead and higher costs compared to managed services.