Showing category results for Machine Learning

We explore how we worked with a customer to add autoscaling capability to a Kubernetes cluster to meet bursty demands for deep learning training in a cost-efficient manner.

The ability to correctly identify entities, such as places, people, and organizations, adds a powerful level of natural language understanding to applications. This post introduces a MIT-licensed one-click deployment to Azure for web services that lets developers get started with a wide range of natural language tasks in 5 minutes or less, by consu...

This post describes in detail the Azure Cognitive Services speech-to-text WebSocket protocol and shows how to implement the protocol in Java. This enables us to transcribe audio to text in near real-time. We then show how to feed the transcribed radio into a pipeline based on Spark Streaming for further analysis, augmentation, and aggregation. The ...

We demonstrate how to train Object Detection models using CNTK and Tensoflow DNN frameworks. Azure ML Workbench is used as the main training and model hosting infrastructure.

We address the challenge of creating a custom search experience for a specific domain area. We also provide a guide for creating your own custom search experience by leveraging Azure Search and Cognitive Services and sharing custom code for iterative testing, measurement and indexer redeployment.

An overview of different image classification approaches including Microsoft Azure Custom Vision Service and CNTK for various levels of classification complexity.

Claiming expenses is usually a manual process. This project aims to improve the efficiency of receipt processing by looking into ways to automate this process.  This code story describes how we created a skeletal framework to achieve the following: We found a few challenges in addressing these goals. For instance, the quality of...

We collaborated on an image classification pipeline to perform automatic face detection and matching using machine learning via Microsoft Cognitive Services Face API. Our project was built with Azure Functions to process images using message queues.

We use IoT sensors to collect positional and motion data from professional and amateur skiers to classify expertise and skill level through machine learning.

Microsoft has partnered with Getty Images to explore how Neural Nets could be used to transform the stock photo industry.