azureml

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

MLOps examples

MLOps using Azure ML Services and Azure DevOps

Operationalize a video anomaly detection model with Azure ML

Example of using HyperDrive to tune a regular ML learner.

Official Azure Reference Architectures for AI workloads

Azure Machine Learning previews

Architecture for deploying real-time scoring of machine learning models using Azure Machine Learning

Distributed Deep Learning using AzureML

ML DevOps using GitHub Actions and Azure Machine Learning

AKS Deployment Tutorial

@microsoft Data Camp (Analytics with Azure Machine Learning)

Batch scoring deep learning models with AML

微软机器学习（Azure Machine Learning）快速入门与实战

Azure Machine Learning Cheat Sheets

A workshop for doing MLOps on Azure Machine Learning

Deploying a Batch Scoring Pipeline for Python Models

This repo has some proposed agenda for Azure Machine Learning related hands-on workshops.

Natural Language Processing. From data preparation to building model and deploy the model to web service

Azure ML + Dask = OSS Data Science & ML @ Scale

Narrow the gap between research and production 😎

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.

The InnerEye-Gateway is a Windows service that acts as a DICOM end point to run inference on https://github.com/microsoft/InnerEye-DeepLearning models.

Workshop interfaces graphiques Azure ML

Samples for use with MLOps

Automating the process of training an ML model using AzureML Python SDK within an Azure Function

This is the Capstone project (last of the three projects) required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we use a dataset external to Azure ML ecosystem. Azure Machine Learning Service and Jupyter Notebook is used to train models using both Hyperdrive and Auto ML and then the best of these models is deployed as an HTTP REST endpoint. The model endpoint is also tested to verify if it is working as intented by sending an HTTP POST request. Azure ML Studio graphical interface is not used in the entire exercise to encourage use of code which is better suited for automation and gives a data scientist more control over their experiment.