Reaching out for some help here.
ManagedOnlineDeployment vs Kube.netesOnlineDeployment
Goal: Host a large number of distinct models on Azure ML.
Description: After throughout investigation, I found out that there are two ways to host a pre-trained real-time model (ie, run inference) on Azure ML.
Details:
What I tried I have 4 running VMs as a result of my creation of 4 real-time endpoints. Those endpoints use Curated Environments that are provided by Microsoft.
VMs
Issues
Build Image > Push Image to CR > Create Custom Environment in AzureML > Create and Deploy Endpoint
If something goes wrong, it only shows when I finish the whole pipeline. It just doesn't feel like the correct way of deploying a model. This process is needed when I cannot use one of the curated environments because I need some dependency that cannot be imported using the conda.yml file
For example: RUN apt-get update -y && apt-get install build-essential cmake pkg-config -y RUN python setup.py build_ext --inplace
Note: Each endpoint has a distinct set of dependencies/versions...
Questions:
1- Am I following the best practice? Or do I need to drastically change my deployment strategy (Move from ManagedOnlineDeployment to Kube.netesOnlineDeployment or even another option that I don't know of)? 2- Is there a way to host all the endpoints on a single VM? Rather than creating a VM for each endpoint. To make it affordable. 3- Is there a way to host the endpoints and get charged per transaction?
General recommendations and clarification questions are more than welcome.
Thank you!
From available options, choose the most useful for the application and create. Then we can deploy.
This procedure will decrease the configuration burden and improve the functionality.
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