A Scalable End-to-End Anomaly Detection System using Azure Batch AI

This post is authored by Said Bleik, Senior Data Scientist at Microsoft. In a previous post I showed how Batch AI can be used to train many anomaly detection models in parallel for IoT scenarios. Although model training tasks are usually the most demanding ones in AI applications, making predictions at scale on a continuous… Read more

Azure Machine Learning Packages for Vision, Text and Forecasting in Public Preview

This post is authored by Matt Conners, Principal Program Manager, and Neta Haiby, Principal Program Manager at Microsoft. Earlier today, at Build 2018, we made a set of Azure AI Platform announcements, including the public preview release of Azure Machine Learning Packages for Computer Vision, Text Analytics, and Forecasting. The Azure Machine Learning Packages are… Read more

Kubernetes Load Testing

This post is authored by Daniel Grecoe, Senior Software Engineer at Microsoft. Today many platforms are moving towards hosting artificial intelligence models in self-managed container services such as Kubernetes. At Microsoft this is a substantial change from Azure Machine Learning Studio which provided all the model management and operationalization services for the user automatically. To… Read more

How to Develop a Currency Detection Model using Azure Machine Learning

This post is authored by Xiaoyong Zhu, Anirudh Koul and Wee Hyong Tok of Microsoft. Introduction How does one teach a machine to see? Seeing AI is an exciting Microsoft research project that harnesses the power of Artificial Intelligence to open the visual world and describe nearby people, objects, text, colors and more using spoken… Read more

Transfer Learning for Text using Deep Learning Virtual Machine (DLVM)

This post is by Anusua Trivedi, Data Scientist, and Wee Hyong Tok, Data Scientist Manager, at Microsoft. Motivation Modern machine learning models, especially deep neural networks, can often benefit quite significantly from transfer learning. In computer vision, deep convolutional neural networks trained on a large image classification datasets such as ImageNet have proved to be… Read more

Deep Learning for Emojis with VS Code Tools for AI

This post is the first in a two-part series, and is authored by Erika Menezes, Software Engineer at Microsoft. Visual content has always been a critical part of communication.¬†Emojis are increasingly playing a crucial role in human dialogue conducted on leading social media and messaging platforms. Concise and fun to use, emojis can help improve… Read more

Deploying Deep Learning Models on Kubernetes with GPUs

This post is authored by Mathew Salvaris and Fidan Boylu Uz, Senior Data Scientists at Microsoft. One of the major challenges that data scientists often face is closing the gap between training a deep learning model and deploying it at production scale. Training of these models is a resource intensive task that requires a lot… Read more

Inventory Optimization Solution in the Azure AI Gallery

This post is co-authored by Dmitry Pechyoni, Senior Data Scientist, Hong Lu and Chenhui Hu, Data Scientists, Praneet Solanki, Software Engineer, and Ilan Reiter, Principal Data Scientist Manager at Microsoft. For retailers, inventory optimization is a critical task to facilitate production planning, cost reduction, and operation management. Tremendous business value can be derived by optimizing… Read more

Intelligent Edge: Building a Skin Cancer Prediction App with Azure Machine Learning, CoreML & Xamarin

This post is authored by Anusua Trivedi, Carlos Pessoa, Vivek Gupta & Wee Hyong Tok from the Cloud AI Platform team at Microsoft. Motivation AI has emerged as one of the most disruptive forces behind digital transformation and it is revolutionizing the way we live and work. AI-powered experiences are augmenting human capabilities and transforming… Read more

Training Many Anomaly Detection Models using Azure Batch AI

This post is authored by Said Bleik, Senior Data Scientist at Microsoft. In the IoT world, it’s not uncommon that you’d want to monitor thousands of devices across different sites to ensure normal behavior. Devices can be as small as microcontrollers or as big as aircraft engines and might have sensors attached to them to… Read more