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

How to Train & Serve Deep Learning Models at Scale, Using Cognitive Toolkit with Kubernetes on Azure

This post is authored by Wee Hyong Tok, Principal Data Science Manager at Microsoft. Deep Learning has fueled the emergence of many practical applications and experiences. It has played a central role in making many recent breakthroughs possible, ranging from speech recognition that’s reached human parity in word recognition during conversations, to neural networks that… Read more

Want an Inside Track on the Very Latest in Data Science, Machine Learning & AI?

We have created a special two-day Data Science, Machine Learning, and AI Pass for Microsoft Ignite in Orlando, on September 25th and 26th, just for you! In addition to full access to all the sessions pertaining to these domains – such as the latest ML and deep learning techniques, open source tools, cognitive services APIs,… Read more

A New Real-Time AI Platform from Microsoft, and a Speech Recognition Milestone

Re-posted from the Microsoft Research blog. Microsoft had a couple of major AI -related announcements earlier this week, summarized below. Project Brainwave, for Real-Time AI Microsoft unveiled Project Brainwave earlier this week. A new deep learning acceleration platform, Project Brainwave represents a big leap forward in performance and flexibility for serving cloud-based deep learning models…. Read more

Machine Learning for Developers – How to Build Intelligent Apps & Services

This post is authored by Daniel Grecoe, Senior Software Engineer at Microsoft. There’s a lot of talk about machine learning these days and how it will transform applications and services. Most of it is right on target: ML is definitely changing the way certain computing tasks will be implemented in the future, why wouldn’t it?… Read more

Introducing the new Data Science Virtual Machine on Windows Server 2016

This post is authored by Udayan Kumar, Software Engineer at Microsoft. We are excited to offer a Windows Server 2016 version of our very popular Microsoft Azure Data Science Virtual Machine (DSVM). This new DSVM version is based on the latest Windows Server 2016 Data Center edition. We’ve added new tools and upgraded existing tools… Read more

GA of Cognitive Toolkit 2.0 – Microsoft’s Open Source, Enterprise-Ready, TensorFlow-Outperforming AI Toolkit

Re-posted from the Microsoft Next blog and the Cognitive Toolkit blog. We’re excited to announce the general availability of Cognitive Toolkit 2.0, Microsoft’s open source, enterprise-ready, production-grade AI offering. Cognitive Toolkit allows users to create, train, and evaluate their own neural networks that can then scale efficiently across multiple GPUs and machines on massive data… Read more

Deployment of Pre-Trained Models on Azure Container Services

This post is authored by Mathew Salvaris, Ilia Karmanov and Jaya Mathew. Data scientists and engineers routinely encounter issues when moving their final functional software and code from their development environment (laptop, desktop) to a test environment, or from a staging environment to production. These difficulties primarily stem from differences between the underlying software environments… Read more