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This project is a component of the Udacity Azure ML Nanodegree. It entails the construction and refinement of an Azure ML pipeline utilizing the Python SDK and a provided Scikit-learn model. The ensuing model is then evaluated against an Azure AutoML run.
Using Azure Machine Learning studio, To create a Linear Regressor model to predict the price of an automobile with its features (like horsepower, peakrpm, engine-type, body-style etc...).
Project made during DIO's "Microsoft Azure AI Fundamentals" Bootcamp, under the section "Reconhecimento Facial e transformação de imagens em Dados no Azure ML"
In this project we use Microsoft Azure Cloud Computing Services to configure a cloud-based machine learning production model, deploy it, and consume it. We will also create, publish, and consume a pipeline.
This project uses Automated ML and a customized model whose hyperparameters are tuned using HyperDrive. The model trained by AutoML will be later on deployed and could be used as a ML service with which we can interact using REST API.