AI-900 -Explore Automated Machine Learning in Azure

 

Explore Automated Machine Learning


Step 1 : Go to the Azure Portal and Sign in using your credentials.

Step 2 : Then in searchbar search for azure machine learning studio.


Step 3 : It will redirect you to azure machine learning page. There click on create and select New workspace.


OR

Step 1 : In chrome or any other browser search for https://ml.azure.com. It will directly redirect you to the Default Directory Page.


Step 2 : From sidebar select workspaces and then click on create. It will redirect you to creating workspace page.



OR

Step 1 : In chrome or any other browser search for https://aka.ms/ml-lab. Then click on Create WorkSpace.


Step 2 : Add the details and click Create.


Step 3 : Click on created lab.


Step 4 : Download the data.
Go to the https://aka.ms/mslearn-ml-data and download the zip file ml-data.zip.
Extract the downloaded ml-data.zip archive to see the files it contains. Note that one of these files is ice-cream.csv, which contains the ice cream sales data required for this exercise.


Step 5 : Now from the sidebar go to the Automated ML page. And click on New Automated ML Job.




Step 6 : Add the details as shown in below image.


Create a new dataset named ice-cream. And upload the extraced ice-cream.csv file. 
Include only the following columns (Date is unique for each row, and adds predictive capability on its own):
    DayOfWeek
   Month
   Temperature
   Rainfall
   IceCreamsSold
And click create.



Ensure your newly created ice-cream data asset is selected before moving to the
next step



Step 7 : Here, we want to predict ice-cream sold. So set the target column as IceCreamSold field.

Set Additional Configuration Settings. And click save.



Set Featurization Settings. And Click save.


Set Limits.


Step 8 : Select Compute Type. Select serverless.



Step 9 : Review the details and Submit.


Step 10 : Review the best model.

On the Overview tab of the job details page, view the information about the job and note the best model summary.

Select the Algorithm name for the best model to view its details. Then on the child job details page, view the following tabs:

Overview: General details for the child job.
Model: Information about the model that was trained.
Metrics Evaluation metrics and visualizations for the model based on the test data used during the training process.
Outputs and logs: Information logged during the training process.












Step 11 : Deploy and test the model. Click on deploy button to deploy the model.
Add the details and click deploy.



Step 12 : Test the deployed model. Go to the Endpoints page from sidebar.



Step 13 : Go to the Test tab.

Replace the Json data to this data.

{ "input_data": { "columns": [ "DayOfWeek", "Month", "Temperature", "Rainfall" ], "index": [0], "data": [["Wednesday","June",70.5,0.05]] } }




Click on Send Test Request.


Step 14 : Explore the consume tab to understand the code.





































































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