Deployment deep learning system with some models with MLaaS

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I read some articles with deployment examples and they were about deploying one model but not a whole deep learning system.

If I want to deploy my project including launch of multiple deep models built with different frameworks (Pytorch, tensorflow) then what's good option for that:

  1. build Docker image with whole project and deploy it with ml service (azure, aws lambda etc);
  2. or deploy every single model with chosen MLaaS and and elsewhere deploy the logic that makes requests to the above models;

I would appreciate any reference/link on the subject. Thanx.

azure-machine-learning-service
1 Answers

We have public open source release of Many Models solution accelerator. The accelerator is now available on GitHub and open to everyone: Many Models: https://aka.ms/many-models. • Check out a blog on Many Models from MTC AI Architect Sam here

Check this document using designer: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-designer? Basically you can register a trained model in Designer bring it out with SDK/CLI to deploy it.

One approach with current integration between Azure ML and Azure DevOps is to setup a release pipeline in Azure DevOps which is triggered by the model registration in your Dev workspace model registry which them deploys to your Prod workspace. There is guidance and examples in this repo https://github.com/Microsoft/MLOpsPython And more general guidance for MLops at http://aka.ms/mlops This also allows for putting integration tests into your release process or other steps like approval processes if needed using DevOps functionality.

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