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.

AzureML framework train, deploy and consume best model

Building and optimizing Azure ML pipeline using Python SDK and a Scikit-learn model.

A Webterm hypercore web crawler

Udacity course Machine Learning Engineer with Microsoft Azure project 1 - Optimizing an ML Pipeline in Azure.

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.

Bank Marketing dataset was used to train a model and later on optimizing it.

Classical Guitar Tablature from classtab.org represented in SLEEP files for HyperDrive

HyperTheater leverages the hypercore protocol to let users like yourself maybe, to create their own "movie house" session and let their friends join and have fun.

This is the final capstone project for Udacity Nano Degree Machine Learning Engineer with Microsoft Azure

TCP/UDP swarm on top of WebExtension's native messaging protocol

[unfinished] – Serve a Wikipedia hyperdrive feed over HTTP.

Helpers for working with hyperdrives

ML Engineering with MS Azure

Example APIs, written with Hyperdrive

The Ara FileSystem

A GraphQL interface over a connected Hyperdrive Daemon Client.

Cloud based models built using Azure AutoML and Logistic Regression with its hyperparameters tuned using HyperDrive. The best model was deployed using ACI with Swagger Documentation.

This is the capstone project for the Udacity Machine Learning Engineer with Microsoft Azure. In this project we analize the Mexican Government's data for the COVID-19 pandemic from January of 2020 up until the 10th of May of 2021. From this data we intend to get a predictive model to analyze if a patient will enter an Intensive Care Unit (ICU) o…

Creating and optimizing an Azure Machine Learning Pipeline for Udacity course