Training a Model with the Azure ML Designer
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- Опубликовано: 24 июл 2023
- In this video, we will train a machine learning model using the Azure ML Designer's drag-and-drop user interface.
LINKS AND INFO
Catallaxy Services - www.catallaxyservices.com
Chicago parking tickets dataset -- cspolybasepublic.blob.core.wi...
Chicago parking tickets database -- sqlsunday.com/2022/12/05/new-... 
Наука
Kevin!
I really appreciate your effort in making this valuable material.
I'm very surprised that you only have 2k subscribers, for such a detailed, clear and relevant information.
Please don't be discouraged, if such is the case
Hi Kevin. Thank you for this. You helped me.
thankyou needed this video
Thank you for this tutorial. I would be more interested in how you generate your fileDataset. Indeed, I have a problem using the dataset I export as MLtable after the labeling phase in Azure ML.
To generate the file dataset, grab the file from the video description and save it locally. Then, upload it into Azure ML as a v1 Tabular type, not a v2 MLTable. In the Data menu, make sure you are on the Data assets tab. Then, select +Create and name the data asset something like ChicagoParkingTickets. Select in the Type menu "Tabular" from the v1 section. On the second page, create your data asset From local files and that will give you a few more pages around where to store the data, the file you want to upload, and additional settings. Those steps should be pretty straightforward, as I tried to ensure that there would be no complications with this dataset.
Uploading an MLTable asset is quite a bit more difficult than the v1 Tabular: learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2&tabs=cli
There's some work behind the scenes to add the MLtable metadata , so that, when I look at the ChicagoParkingTickets dataset in the Azure ML UI, I see the Dataset type = Tabular and Type = Table (mltable). That's why the output node for the Azure ML Designer says MLTable even though I never explicitly generated any MLTable metadata. Azure ML did the work for me after I uploaded the text file as a v1 Tabular dataset.
Thank you for video, but I encountered an error. I did everything exactly as in the video, selected the same computing cluster. But not a single block is executed with an error: UserError: The specified DSI cluster type is not supported for job submission.
There is no information on the Internet about this. I tried to run the built-in samples, the same thing, even clearing the data does not work. I am using a free trial subscription.
Hmm, that is a good question. It could very well be that you're using a trial subscription, but I'm not positive about that because I've not used the free trial subscription for any Azure ML testing.
There's a GitHub post that does walk you through how you can see which VM classes you can use with the free trial: github.com/MicrosoftDocs/azure-docs/issues/56032. These commands use the Azure cloud shell and PowerShell. The idea would be that you could see which VM classes are enabled and what the quotas look like. Then, change the cpu-cluster to use one of the allowed classes and try again. The work I show in this video isn't particularly compute-heavy, so it should still work okay on a single instance of a smaller VM class.
Can you help me please? , Should we only upload the dataset without the Python code or what should we upload, please explain it to me, i have project !
You upload the dataset. To generate the file dataset, grab the file from the video description and save it locally. Then, upload it into Azure ML as a v1 Tabular type, not a v2 MLTable. In the Data menu, make sure you are on the Data assets tab. Then, select +Create and name the data asset something like ChicagoParkingTickets. Select in the Type menu "Tabular" from the v1 section. On the second page, create your data asset From local files and that will give you a few more pages around where to store the data, the file you want to upload, and additional settings. Those steps should be pretty straightforward, as I tried to ensure that there would be no complications with this dataset.
The Python code is something you submit via API call and we do that in the next video in the series.
And is this classifcation model?? What about the concept of best model generated ?
If you're talking about the best model generated from my prior AutoML video, we're training a new model from scratch to see how to do it.
If you're asking in general, you can do this in a couple of ways. One is to train separate models as different experiment runs, saving each in the Azure ML model registry and comparing model results--for classification, you might check measures like accuracy and F1 score. A second option would be to train separate models as runs in an experiment but tagged under the same model type, saving different versions of a model in the registry. Then, after comparing, you could delete the versions that don't perform as well. A third option would be to perform comparative model analysis as part of your initial training: you can incorporate hyperparameter sweeping and even use of different algorithms in the training steps and then save the best model of the bunch to the registry. I don't have an example of doing this in a video but Microsoft does have a code example of using a hyperparameter sweep: github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1c_pipeline_with_hyperparameter_sweep