I have some models running in AI-Platforms under GCP which are serving predictions without a problem. Now I am trying to automate this deployment process using kubernets pipelines so that model version gets updated periodically. I tried to create some pipelines using available samples but non of these are for AI platforms. The training of the model has been handled by AI-Platform Jobs with following parameters:

• Python: 3.7
• Framework: Tensorflow
• Framework version: 2.1
• ML runtime version: 2.1

Trained model are being created parodically and are being saved in buckets.
How can I automate this deployment process using pipelines. If there is another alternative approach for this automation, I would like to try it as well.