How to update Sagemaker Endpoint with the newly Trained Model?

Question

I am able to train a model on Sagemaker and then deploy a model endpoint out of it.

Now, I want to retrain my model every week with the new data that is coming in. My question is - when I retrain the model how do I update my existing endpoint to use the latest model. (I don't want to deploy a new endpoint)

From some exploration, I think I can do it in 2 ways -

1. Near the end of the training job, I create a new EndpointConfig and later use UpdateEndpoint - The downside of this would be - I would end up with a lot of unnecessary Endpoint Configurations in my AWS Account? Or am I thinking about it wrongly?

2. Near the end of the training job, I deploy the trained model using .deploy() and set update_endpoint=True as illustrated in Sagemaker SDK Doc

I am not sure which is the better solution to accomplish this? Is there an even better way to do this?

Answer 1

If you are interested in doing this programmatically, use an AWS SDK (I will answer this assuming you are using Java.

Look the AWS SDK for Java V2 Javadocs. You can use the UpdateEndpoint to perform this use case. This method deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).