![]() ![]() ![]() Now you're ready to build a machine learning model. Then select Shift + Enter (or select Control + Enter or select the Play button next to the cell). For example, in the cell you can type the following code: import numpy as np The compute instance can take 2 to 4 minutes to be provisioned.Īfter the compute is provisioned, you can use the notebook to run code cells. This color change indicates that the compute instance is being created: In the notebook, you might notice the circle next to Compute turned cyan. Valid characters are uppercase and lowercase letters, digits, and hyphens (-). On the Configure Settings page, provide a valid Compute name.For this tutorial, you can choose a Standard_D11_v2, with 2 cores and 14 GB of RAM. Start by selecting the plus icon at the top of the notebook: Next, to run code cells, create a compute instance and attach it to your notebook. Name your notebook (for example, my_model_notebook).On the Azure Machine Learning Studio home page, select Create new > Notebook: Introductory knowledge of the Python language and machine learning workflows.If you don't already have a workspace, see Create workspace resources. If you don't already have a subscription, you can use a free trial. Deploy the model to a real-time scoring endpoint.Write a scoring script that defines the input and output for easy integration into Microsoft Power BI.Train a regression model by using scikit-learn.Create an Azure Machine Learning compute instance. ![]()
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