[sanketsarang]

That is not true actually. You must purchase an Azure VM for training a model. Deploying trained models on your own infrastructure is permitted. Reasonably speaking we would have to call this an enterprise solution, as there is no way to get a trained model without paying Azure fees. Most models will require GPU, and it is not like we can run AzureML on the free GPU offered by Google Colab.

[Avalaxy]

?

https://docs.microsoft.com/en-us/azure/machine-learning/conc...

Not sure where you get your information from. It says it right here, you can use "local computer" for training your model, and that includes AutoML. There is no cost to using Azure ML.

[sanketsarang]

So what we understand from our study is the following:

1. You must have an Azure account with an API key for using their AutoML. An AutoML environment won't get created without a valid key. Both local and cloud runs mandate this.

2. Getting an Azure account requires a credit card on file and comes with a limited-time free trial. This is a big NO NO when it comes to software claimed to be for free use.

3. The free for life services do not call out AutoML anywhere. They do not claim the AutoML environment (a required step) to be free in any form. Check this: https://azure.microsoft.com/en-gb/free/

4. When they say "local" what are they referring to? Run locally on an Azure Notebook, or can I run this locally on my laptop? We have tried and failed ever to get this to run locally on a laptop. So it is not clear whether this is even possible, or the term "local" is misleading.

Have you managed to run Azure ML locally on a laptop, without requiring a connected Azure account?

Yes, if we run the AutoML from our laptop, it uses the API key to create a cloud instance. The data gets uploaded to the cloud and runs on the cloud. Results are thrown back to the local code. We would not call this a local run.

The question is, have you managed actually to use your computer's local resources for training? If so, please do share how this was possible. We would like to know how this was achieved.