Deep Learning Without Labels

Announcing new open source contributions to the Apache Spark community for creating deep, distributed, object detectors – without a single human-generated label This post is authored by members of the Microsoft ML for Apache Spark Team – Mark Hamilton, Minsoo Thigpen, Abhiram Eswaran, Ari Green, Courtney Cochrane, Janhavi Suresh Mahajan, Karthik Rajendran, Sudarshan Raghunathan, and… Read more

Demystifying Docker for Data Scientists – A Docker Tutorial for Your Deep Learning Projects

This post is authored by Shaheen Gauher, Data Scientist at Microsoft. Data scientists who have been hearing a lot about Docker must be wondering whether it is, in fact, the best thing ever since sliced bread. If you too are wondering what the fuss is all about, or how to leverage Docker in your data… Read more

Comparing Deep Learning Frameworks: A Rosetta Stone Approach

This post is authored by Ilia Karmanov, Mathew Salvaris, Miguel Fierro, Danielle Dean, all Data Scientists at Microsoft. With this blog post, we are releasing a full version 1.0 of this repo, open-source on GitHub at: https://github.com/ilkarman/DeepLearningFrameworks. We believe deep-learning frameworks are like languages: Sure, many people speak English, but each language serves its own… Read more

Pixel-Level Land Cover Classification Using the Geo AI Data Science Virtual Machine and Batch AI

This post was authored by Mary Wahl, Kolya Malkin, Siyu Yang, Patrick Flickinger, Wee Hyong Tok, Lucas Joppa, and Nebojsa Jojic, representing the Microsoft Research and AI for Earth teams. Last week Microsoft launched the Geo AI Data Science Virtual Machine (DSVM), an Azure VM type specially tailored to data scientists and analysts that manage… Read more

ONNX Models to be Runnable Natively on 100s of Millions of Windows Devices

This post was authored by Eric Boyd, CVP, AI Data & Infrastructure. Today Microsoft is announcing the next major update to Windows will include the ability to run Open Neural Network Exchange (ONNX) models natively with hardware acceleration. This brings 100s of millions of Windows devices, ranging from IoT edge devices to HoloLens to 2-in-1s… Read more

Image Data Support in Apache Spark

This post is co-authored by the Microsoft Azure Machine Learning team, in collaboration with Databricks Machine Learning team. Introduction Apache Spark is being increasingly used for deep learning applications for image processing and computer vision at scale. Problems such as image classification or object detection are being solved using deep learning frameworks such as Cognitive… Read more

Hello 2018 | Recap of “Top 10” Posts of 2017

As we ring in the new year, we’d like to kick things off in our usual fashion – with a quick recap of our most popular posts from the year just concluded. So here are our “Top 10” posts from 2017, sorted in increasing order of readership – enjoy! 10. Quick-Start Guide to the Data Science… Read more

Free Webinars on Cognitive Toolkit with Batch AI, DSVM & Document Collection Analysis

Join us at a set of three exciting webinars starting on Tuesday next week where we’ll show you how to train distributed convolution neural networks using Microsoft Cognitive Toolkit (aka CNTK) and Batch AI, how to do AI development using the latest version of the Data Science Virtual Machine (DSVM), and how to use Document… Read more

Announcing the Data Science Virtual Machine in Batch AI Service

This post is authored by Paul Shealy, Senior Software Engineer at Microsoft. We are pleased to announce the integration of the Microsoft Data Science Virtual Machine (DSVM) with the Batch AI service in Azure. DSVM is a family of popular VM images published on Azure with a broad choice of machine learning, AI and data… Read more

Introducing Microsoft Machine Learning Server 9.2 Release

This post is authored by Nagesh Pabbisetty, Partner Director of Program Management at Microsoft. Earlier this year, Microsoft CEO Satya Nadella shared his vision for Microsoft and AI, pointing to Microsoft’s beginnings as a tools company, and our current focus on democratizing AI by putting tools “in the hands of every developer, every organization, every… Read more