You guys are right, the workspace is definitely the first thing you should create. Although, I have to admit, a Jupyter notebook could also be helpful for playing around with the automated ML features. But the workspace is still the essential starting point.
Yeah, I was thinking the same thing. Creating the workspace first just makes the most logical sense. Once you have that set up, then you can start working on the other pieces like the dataset and the designer pipeline.
I agree with Adell. The Machine Learning workspace is the foundation you need to build everything else on. Without that, you won't be able to access the automated ML tools or any of the other Azure ML services.
Hmm, this seems like a straightforward question. I think the answer is B) a Machine Learning workspace. That's the core component you need to set up before you can start using Azure Machine Learning Studio and automated ML.
Hmm, this is a tricky one. I suppose the first step would be to create a Machine Learning workspace, right? That seems like the logical starting point to me.
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