Multi-model deployment in AWS Sagemaker | MLOPS | Pytorch

Multi-model deployment in AWS Sagemaker | MLOPS | Pytorch

Deploy multiple models in same server using sagemaker and save costs on model deployment. I will walk you through different ways to deploy. Multi-model deployment in AWS Sagemaker | MLOPS | Pytorch.

If you have ever deployed a computationally heavy AI model, you are probably aware of its price to deploy. Not necessarily an AI model; it can be any model to run in production around the clock.

I have few pytorch models in production, and it is so expensive over time irrespective of any platforms I use. So I decided to reduce my cost of model deployment. And I found AWS sagemaker has a multi-model deployment option. However, the docs are not super friendly and often confusing. So I decided to explain a bit more in this post.

If you are reading through this article, I assume that you are aware of AWS sagemaker and can deploy models in the platform. If not, please refer to this article to go over it in detail.

Things you need for the setup

  1. AWS account with sagemaker access
  2. ECR docker container access
  3. Sagemaker notebook or local jupyter notebook environment

aws-sagemaker aws machine-learning mlops

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