Machine Learning migration

Question

now I would like to migrate the machine learning component from 'dev environment' to 'prd environment', may I know how I can do step by step， thanks。

Answer 1

With Azure Machine Learning Service, once the data scientist builds a satisfactory model, the trained model can be easily put into production and monitored.

The following diagram illustrates the complete deployment workflow (compare Amazon AWS with Microsoft Azure):

Amazon AWS:

Microsoft Azure:

• Keep in mind the workspace, which represents a central location for a team to collaborate and it manages access to compute targets, data storage, models created, docker images created, webservices deployed and it keeps track of all the experiment runs that were performed with it. Data scientists can manage the authorization and creation of workspaces and experiment from the Python SDK.

Basically, you should perform the following steps:

1. Register the model in a registry hosted in your Azure Machine Learning Service workspace
2. Register an image that pairs a model with a scoring script and dependencies in a portable container
3. Deploy the image as a web service in the cloud or to edge devices

Answer 2

Hello @Seiya Sum

Thanks for reaching out to us. I think you are mentioning how to register and reuse your Azure Machine Learning Component. Components are automatically shared with users in the same workspace. You can reuse components across pipelines, environments, workspaces, and subscriptions. Built-in version-tracking lets you keep track of changes and reproduce results.

To do that, you need to register your created component as below guidance and reuse your component by changing the YAML file.

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-component-pipelines-cli#register-components-for-reuse-and-sharing

Hope this helps.

Regards,
Yutong

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