Natural Language Processing Best Practices & Examples

Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

All the available resources to master MLOPS from scratch

Use QLoRA to tune LLM in PyTorch-Lightning w/ Huggingface + MLflow

Kedro plugin to support running workflows on Microsoft Azure ML Pipelines

MLOps samples and docs from real world projects in manufacturing industry

Hands on lab for Neo4j and Microsoft Azure

Get started with Automated Machine Learning (AutoML) and Machine Learning Operations (MLOps) in Azure Machine Learning

An E2E solution of the Data Resources on Azure using the Snapshot Serengeti dataset. This E2E solution focuses Azure Synapse Analytics, Power Bi & the Azure Data Factory.

Ready to use scoring engines for Image, Text and Time Series

Deploy and Serve Model using Azure Databricks, MLFlow and Azure ML deployment to ACI or AKS

The Vitastic solution accelerator provides a pre-packaged solution to build web interfaces that serve object detection models deployed in Azure ML or Custom Vision with customizable themes.

This article presents a reference architecture to enhance the compatibility of Siemens Industrial Artificial Intelligence (Industrial AI) products with Microsoft Azure.

Audio Analytics with Azure Machine Learning

This repository hosts the Azure Tech Blog, authored by Microsoft Korea employees, sharing insights, best practices, and real-world experiences with Azure.

An object detection (with Keras and YOLOv3) MLflow example

A demand forecasting pipeline deployed on Azure and AWS

Application merges WebGL technology with Three.js for 3D rendering and Azure ML for AI chat functionalities, empowers users to interact with realistic 3D models that demonstrate step-by-step procedures. The goal is to provide intuitive visual aids that simplify the setup process reduce user errors, and enhance overall user satisfaction 👾

Exemple AutoML avec Azure ML service SDK

This project is part of the Udacity Azure ML Nanodegree. In this project, we use Azure to configure a cloud-based machine learning production model, deploy it, and consume it. We also create, publish, and consume a pipeline.

Sound event detection

The Near Real-time Fraud and Compliance Analytics Accelerator simplifies the fraud detection and reporting process, cutting down the time to action to prevent fraud as well as enables near real-time dashboards and analytics for streaming data.

Notebooks de démonstration Azure ML service

Code for my blog post on deploying a machine learning model with Azure ML Pipelines

An example about how to train a tree-based decision model using Julia (1.7.2) for the popular Iris dataset in Azure ML.

DeOldify is a projet to colorize and restore old images and film footage

Notebooks Python Azure ML service SDK pour préparation et transformation de données

Classifying Iris with Decision Forest Algorithm

Projects that Unleash the Power of AI to Solve Real-World Business Challenges

A machine learning experiment to predict NHL penalty minutes based on matchups using Azure Machine Learning and automated machine learning