Narrow the gap between research and production 😎

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

Udacity Project: Operationalizing Machine Learning

Capstone project for pursuing "Audacity's Machine Learning Engineering with Microsoft Azure Nanodegree". This project is about of predicting the risk to died in circunstances related to homicides in El Salvador

Azure ML + Darknet for YOLOv4

Anomaly-based intrusion detection in computer networks using supervised machine learning

Microsoft Internship Program: During this Internship, I have worked on projects related to some of the machine learning algorithms. And deployed the model using Microsoft Azure.

Azure ML workspace with a managed VNET and private endpoints

Dentro del proceso de la ciencia de datos, la limpieza de datos suele ser la etapa que más tiempo consume. A menos que sea necesario por limitaciones físicas, completar toda esta tarea con computo en la nube puede ser una vía poco económica a comparación de hacerlo on-premise. En esta ocasión, aprenderemos como hacerlo a través de un simple ejem…

FineTune Microsoft Phi-2 with your own data

Train & track a model using Databricks & MLflow, triggering a Azure DevOps* build pipeline with each model stage change, downloading the model artifacts from MLflow, building a Docker container to package them and pushing this container to Azure Container Registry.

Udacity Azure ML Nanodegree - Assignment 1

This is second of the three projects required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we create, publish and consume a Pipeline. We also explore ML model deployment as an HTTP REST API endpoint, swagger API documentation, apache benchmarking of the deployed endpoint and cons…

Short tutorials on how to use Azure services

⚡ Supercharge your home with AI 🧠

Repository to explore Hugging Face models usage for local development, and for deployment to Azure.

In this project, we create an AutoML experiment, deploy the best model and evaluate its endpoint by consuming it. We also explore the stability and performance of the endpoint by enabling the logs and benchmarking the endpoint.

Azure ML + Power App Solution sample and walkthrough to complement the Azure Architecture Centre reference pattern

In this project we use Microsoft Azure to configure and deploy a cloud based Machine Learning model. We also see how to create, publish and consume a pipeline using Azure SDK.

A repo that shows how to use Azure Machine Learning with Azure Files NFS