With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates.

This project is part of the Udacity Azure ML Nanodegree. In this project, we build and optimize an Azure ML pipeline using the Python SDK and a provided Scikit-learn model. This model is then compared to an Azure AutoML run.

This project aims to create Machine Learning models using Azure's AutoML to find the best model that fits the data and Hypderdrive to find the best hyperparameters.

Docker deployment of AzureAutoML model using Flask.

Transformations and preprocessing of data to run Azure AutoML on evaluating machine learning models to predict whether a bidding item registered in purchases from ComprasGov systems will not have interested suppliers.

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.

Optimizing Machine Learning Pipeline in Microsoft Azure - Udacity Machine Learning Engineer with Microsoft Azure Nanodegree Program (Project 1)

This project demonstrates a machine learning pipeline using Azure ML for model optimization. It compares HyperDrive-tuned Scikit-learn Logistic Regression with Azure AutoML’s automated model selection. Includes code, documentation, and performance comparisons, showcasing Azure ML's model management and optimization capabilities.

In this project, we build and optimize an Azure ML pipeline using the Python SDK and a Scikit-learn model. This model is then compared to an Azure AutoML run.

Repo for the "Building a No-Code Classification Model with Azure Automated ML" hands-on lab.