Azure ML is an end-to-end, cloud-based, advanced/predictive analytics platform. It caters to organizations, users, and data scientists of all skill-levels and experience. Azure ML allows users to import training data, build, train, and deploy machine learning models, and even predict outcomes and cluster data all from a simple web browser.
In addition, users can easily import training data from a number of sources including flat files, Azure BLOB storage, Hive queries, Azure SQL Databases, website lists, and more. They can then use the simple, familiar drag-and-drop interface to create common workflow tasks for prepping data, selecting features, and training, scoring, and comparing models.
This page will explain the step-by-step process to upload the file from local machine and train a classification model with no-code AutoML using Azure Machine Learning automated ML in the Azure Machine Learning studio.
- Azure Cloud Account
- Azure Subscription and Resource Group
Enter your email id and password. Then you will be redirected to Azure Portal Page.
Now click on “Create a Resource”. Type “Azure Machine Learning” in the search box and select the first option.
Azure Machine Learning Dashboard will pop up. Click on “Create” Button. It will ask you to fill some information.
“Workspace Name”: Name of the environment/workspace”
“Subscription”: Select the subscription.
“Resource Group”: Select the Resource Group
“Region”: Choose your location.
“Storage Account”: Create a new Storage account.
“Key Vault”: Create a new key Vault.
“Application insights”: Create a new Application insights.
“Container registry”: Select None
Then click on “Review and Create” button. It will review your all inputs. If all are correct, then it show check sign in each field.
Now, if all field inputs are valid, then click on “Create” button.
This will start creation of workspace and take you to the deployment page. The deployment will be in process. Wait for few seconds. Once the deployment is finished it will show the message ”Your deployment is complete”. Now click on “Go to Resource” button.
It will show your workspace overview page. From here you will be able to control your whole workspace. Click on “Launch Now”.
The new tab will open, it’s the workspace that we have created. There are various option on left side.
Create a new dataset by selecting From local files from the +Create dataset drop-down.
On the Basic info form, give your dataset a name and provide an optional description. The automated ML interface currently only supports Tabular Datasets, so the dataset type should default to Tabular.
On the Datastore and file selection form, select the default datastore that was automatically set up during your workspace creation, workspaceblobstore (Azure Blob Storage). This is where you’ll upload your data file to make it available to your workspace.
Select Upload files from the Upload drop-down.
Select Next on the bottom left, to upload it to the default container that was automatically set up during your workspace creation.
When the upload is complete, the Settings and preview form is pre-populated based on the file type.
The Schema form allows for further configuration of your data for this experiment. For this example, select the toggle switch for the day_of_week, so as to not include it. Select Next.
On the Confirm details form, verify the information matches what was previously populated on the Basic info, Datastore and file selection and Settings and preview forms.
Select Create to complete the creation of your dataset.
Select your dataset once it appears in the list.
Review the Data preview and Close.
After the Dataset has been registered, next step is to design the Experiments as, selecting the size of your compute environment and specifying what column you want to predict.
Select ‘Automated ML’ and Create New Automated ML run.
Select the Dataset from the list and Click NEXT. Configure run by creating New Experiment.
“New Experiment Name”: Name of the experiment”
“Target Column”: Select the target column.
“Select Compute Type”: Select the Compute resource as- Compute Cluster.
“Select Azure ML compute cluster”: Choose your location.
Populate the Select virtual machine form to set up your compute.
Select Next to populate the Configure settings form.
Select Create to create your compute target.
On the Select task and settings form, complete the setup for your automated ML experiment by specifying the machine learning task type and configuration settings.
Select View additional configuration settings and populate the fields as follows. These settings are to better control the training job. Otherwise, defaults are applied based on experiment selection and data.
Next Validate and test form and Click Finish.
The Run Detail screen opens with the Run status at the top as the experiment preparation begins. This status updates as the experiment progresses.
Once the Model has been created, we can deploy the Model as a Web Service.