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 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.

Audio Analytics with Azure Machine Learning

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

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

An object detection (with Keras and YOLOv3) MLflow example

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

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 👾