From the course: Microsoft Azure Data Scientist Associate (DP-100) Cert Prep

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Use MLflow to log metrics from a job run

Use MLflow to log metrics from a job run - Azure Tutorial

From the course: Microsoft Azure Data Scientist Associate (DP-100) Cert Prep

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Use MLflow to log metrics from a job run

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- [Instructor] Azure ML CLI extension version two has many commands that allow you to efficiently interrogate the entire product. Let's take a look at some of these commands. For example, managed Azure ML batch deployments, delete deployments, list deployments. There's a whole list of operations that are, in many cases, much more simple to investigate than to click on a GUI. Let's go ahead and play around with this ourselves. So if I go over to Azure ML Studio, an easy way to get to the CLI and get everything hooked in is to be logged into your workspace, and then to select terminal. If we go ahead and we select terminal, right here, we get this welcome to Azure Machine Learning terminal, which is really nice because we can immediately start playing around with this interface. So the way that you can start is type in az ml --help, and this gives you a list of all of the commands that are at your disposal. You can see the subgroups here are things like batch deployment, compute, data…

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