Azure Machine Learning Task, Modules & Workflow

List of machine learning Tasks by category

  • Initialize ModelInitialize ModelChoose from a variety of customizable machine learning algorithms, including clustering, regression, classification, and anomaly detection model.

    Train Model 

  • Train

    Provide your data to the configured model to learn from patterns and create statistics that can be used for predictions.

Score

Score

Create predictions using the trained models.

Evaluate

Evaluate

Measure the accuracy of a trained model, or compare multiple models.


The typical workflow in Azure machine learning includes many phases:

  • Identifying a problem to solve and a metric for measuring results.
  • Finding, cleaning, and preparing appropriate data.
  • Identifying the best features and engineering new features.
  • Building, evaluating, and tuning models.
  • Using models to generate predictions, recommendations, and other results.

The modules in this section provide tools for the final phases of machine learning, in which you apply an algorithm to data to train a model. In these final phases, you also generate scores, and then evaluate the accuracy and usefulness of the model.


By following the above mentioned workflow and different modules we can make such Forecasting Experiment.

Sample model.PNG

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