So I have a pre-trained ML model stored in an S3 bucket (nameexample.tar.gz), I have a pipeline, and it will run terraform to create a model by using this file on SageMaker and get prediction, my question is what's the best practice of version control the created-model on SageMaker?
My thought is I manually set this model artifact a name like example-v1.tar.gz, meaning this is the first artifact we will deploy, then because my pipeline can version control the terraform code (build a terraform-v1.0.0.zip file) then execute this file to create the model on SageMaker, my question is basically should I just use model-v1 as model name on SageMaker or should I update this model name frequently, like everytime there's a new-versioned terraform zip file, we use the same version number for the model name on SageMaker (model-v1.0.0)?
I'm new to ML model deployment, a bit confused about this, hope this makes sense, can someone help me?Thanks.
