Machine learning solution requires an infrastructure starting from preparing dataset to deployment. Azure services brings complement solution in the form of Analytics services. Analytics is discovery, interpretation and communication of meaningful patterns in data. Azure analytics provide wide range of services and infrastructure to build solution of any machine learning problem in an efficient and effective way. Below diagram depict high level overview of Azure machine learning environment.
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High level overview of Azure Machine learning environment |
Machine Learning flow of events VS Azure ML : As stated earlier solution to any ML problem begins with preparing data, algorithm selection, training model and evaluation. Below is comparison of ML workflow with Azure ML modules which facilities which provides solution in no time with just vanilla drag & drop and interconnect workflow.
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Normal ML Workflow paralleled with Azure ML Modules |
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Azure ML Core components |
Auto Price prediction Experiment : Here we will use Azure ML widgets and create an experiment and evaluate accuracy of system with test data.
In this experiment we are using Automobile price data from sample data(bundled in ML Studio) followed by select columns and clean missing data. Once data has been pre-processed we select linear regression algorithm which is feed to train model with training data set. Finally we use test data set to test this model. All these widgets dragged from left panel and inter connect each other.
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Azure ML Experiment predicting Auto price |
Test data set result: On successful run of this experiment, we can visualise result in form of co-efficient of determination.
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Automobile experiment evaluation result |
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