You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace. You need to submit a pipeline run from the designer.
I've got this! The key is to create an attached compute resource first, so I have the infrastructure in place to run the pipeline. Then I can just submit the run and let Azure Machine Learning handle the rest.
Okay, let's think this through. I know I need to submit a pipeline run, so I'll probably need to create an experiment first to hold the run. Then I can select a model and attach a compute resource to run it on.
Hmm, I'm a little unsure about this one. Do I need to create a compute cluster first, or can I just use an attached compute resource? I'll have to double-check the documentation on that.
This seems like a straightforward security-focused question. I'll think about the key security requirements for a cloud solution and which testing approach would be most relevant.
Hmm, this is a tricky one. I know the data protection laws in Hong Kong can be pretty strict, so Kelvin could be facing some serious consequences. I'll need to think through the specific penalties outlined in the ordinance to determine the most likely outcome.
You know, I was thinking the same thing. Creating an attached compute resource sounds like the right way to go. That's like setting up the infrastructure before you can run anything.
Hmm, this is a tricky one. I think we need to create a compute cluster first before we can submit a pipeline run. That's the foundation we need to get started.
Nell
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