Entirely personal preference. They'll both work the same way, so the only reason I prefer the YAML version at all is because I can also use similar files with the az cli and run R or .NET code to perform similar deployments without changing quite as much.

I think I can do that for future videos. I know I typically have the gain turned way down to minimize outside noises but I should be able to do something for the videos.

The Pipeline folder is separate from the Notebook folder. All of the code is available at github.com/feaselkl/Beyond-the-Basics-with-AzureML. Inside the Pipeline folder, we have three sub-folders: - config/ -- This stores the YAML files that we use for each step in the process. - scripts/ -- This stores the Python scripts that we use for each process. Because I am a simple man who tries not to complicate things too overly much, we have one Python script per config file and the names are very similar. - data/ -- This contains a couple data files we use for testing purposes. We also have two scripts, deploy-train.py and deploy-score.py, which handle training and scoring, respectively. There's no strict requirement to use this folder structure with Azure ML, though it does follow some reasonable practices around separating YAML and code.

@@KevinFeasel Hello Kevin, Thanks for the detailed response, I appreciate it. I apologize for not being a bit more clear because you explained most of this directory structure in your video already. What I actually want to know is where your repository, 'beyond-the-basics-with-azureml' lies within the AzureML filesystem? Is it a folder within Authoring > Notebooks -> Users/username? I'm not super familiar with Azure ML yet, so I'm not sure if there is another file system somewhere. In the previous video, I took your advice and used VScode so that I could commit my notebook to a Github repo. What ended up happening is it published the entire folder as a repo, so everything within my compute instance (which is Logs, and Users/username/ChicagoParking.ipynb). Since the video only shows us the directory structure inside the repo, I want to know what is the parent folder of 'beyond-the-basics-with-azureml', so I can organize my repos in a more professional way. Thanks again, I love these videos so far!

@@BusinessDuck69 Ah, I understand the question now. The short answer is, you don't need to upload the files at all. We run the code locally, rather than on our compute instance. We ran the notebook on the compute instance because that infrastructure is already there. Locally, I just have them in a \SourceCode\ folder that holds all of my Git repos. That said, if you want to run the code from your compute instance rather than local machine, you could upload the files into /Users/{username}/beyond-the-basics-with-azureml/, like you have it. Ultimately, think of the compute instance as a data science VM, and the storage associated with it is accessible across the team, but more as a set of scratch files. So admittedly, my AML folder structures sometimes look like a bit of a mess. The "real" outcome should be whatever you put into source control.

You should have the az cli installed beforehand, yes. I have the pre-requisites in the readme file for the demo code: github.com/feaselkl/Beyond-the-Basics-with-AzureML When it does run, it will use your local Python installation, yes. But that will then make a call to the Azure ML API and request executing code on a compute instance there, so you're not doing actual model training locally, just orchestrating it locally.

You are correct that I don't specify the directories for output data for the steps. I do specify the input directory in deploy-train.py's build_pipeline() function on lines 85-100. For example, line 86 has a call to replace_missing_values(input_data=raw_data), where raw_data is the ChicagoParkingTicketsFolder folder input I created in Azure ML. The output location of this first step is something Azure ML injects during my call, not something I explicitly provide. I do, however, have a pointer to wherever that location is, using {step_name}.outputs.output_data. That's how I'm able to chain each of the steps together, and within each Python script, I grab the input and output data as arguments that the central calling code generates after I submit a job.

@@KevinFeaselWonderful, thank you for the explanation. So, there's no need to explicitly provide values for the component's outputs!

Sorry for the late response--I just caught this. It looks like the examples Microsoft has provided are around the v1 SDK. Examples: learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.remote.remotecompute?view=azure-ml-py github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb I haven't seen any examples using the v2 SDK, though.

I was fairly sloppy with "argument" versus "parameter" in that video, not following the rule that parameters are the labels defining variables and that arguments are the actual values passed in. There's a good chance that, as a result, I've used the wrong term at least once in the video. It's something I'll aim to improve in future videos.

@@KevinFeasel Thank you for your answer, the video is great and I understood only parts of it. I am not writing about your specific use in this video and write about parameter and argument more generally. I know parameter and argument is used interchangeable in the whole programming community in our world. And it is annoying me. If I call a method then call what I send into the method parameters. The method needs arguments to do it's job. Yes. They are parameters sent to the method. I am not complaining your use of parameter and argument. The actual values outside the method (or function) are parameters and when they arrive inside method they are arguments, I think. The actual values from variables are now in the method, as you correctly point out. Example: Main() and command-line arguments: static void Main(string[] args){...}. This is what we learn! Why not like this: static void Main(string[] params){...}.