Video – ThyssenKrupp Uses Predictive Analytics to Give Burgeoning Cities a Lift

This is our second post in a series on how Microsoft customers are gaining actionable insights on data by operationalizing ML at scale in the cloud. Based on an IoT (Internet of Things) case study, this post is by Vinod Anantharaman, Head of Business Strategy at Microsoft’s Information Management and Machine Learning (IMML) team. Urban… Read more

Online Learning and Sub-Linear Debugging

This blog post is authored by Paul Mineiro, Research Software Developer at Microsoft. Online learning algorithms are a class of machine learning (ML) techniques that consume the input as a stream and adapt as they consume input. They are often used for their computational desirability, e.g., for speed, the ability to consume large data sets,… Read more

KDD – Two Themes

This blog post is authored by Jacob Spoelstra, Director of Data Science at the Information Management & Machine Learning (IMML) team at Microsoft. The recently concluded KDD conference reaffirmed its claim as the premier conference for Data Science, for both theory and practice, as evidenced by the sold-out crowd of over 2000 that packed the… Read more

Extensibility and R Support in the Azure ML Platform

This blog post is authored by Debi Mishra, Partner Engineering Manager in the Information Management and Machine Learning team at Microsoft. The open source community practicing machine learning (ML) has grown significantly over the last several years with R and Python tools and packages especially gaining adoption among ML practitioners. Many powerful ML libraries have… Read more

Microsoft Machine Learning Hackathon 2014

This blog post is authored by Ran Gilad-Bachrach, a Researcher with the Machine Learning Group in Microsoft Research. Earlier this summer, we had our first broad internal machine learning (ML) hackathon at the Microsoft headquarters in Redmond. One aspect of this hackathon was a one-day competition, the goal of which was to work in teams… Read more

From Stumps to Trees to Forests

This blog post is authored by Chris Burges, Principal Research Manager at Microsoft Research, Redmond. In my last post we looked at how machine learning (ML) provides us with adaptive learning systems that can solve a wide variety of industrial strength problems, using Web search as a case study. In this post I will describe… Read more

Video – Pier 1 Imports Uses Azure ML to Build a Better Relationship with their Customers

This is the first in a series of posts on how Microsoft customers are gaining actionable insights on their data by operationalizing Machine Learning at scale in the cloud. With over 1,000 stores, Pier 1 Imports aims to be their customers’ neighborhood store for furniture and home décor. But the way customers are shopping is… Read more

Machine Learning, meet Computer Vision – Part 2

This blog post is co-authored by Jamie Shotton, Antonio Criminisi and Sebastian Nowozin of Microsoft Research, Cambridge, UK. In our last post, we introduced you to the field of computer vision and talked about a powerful approach, classifying pixels using decision forests, which has found broad application in medical imaging and Kinect. In this second… Read more

Exploration: Data Science… with Mario Garzia

This blog post is authored by Mario Garzia, Partner Data Sciences Architect, Technology and Research Data Science and “big data” have become 21st century buzzwords in the tech industry. Yet in many ways the term “Big Data” is relative to our ability to collect, store and process data. Big data challenges are not new, historically… Read more

Vowpal Wabbit for Fast Learning

This blog post is authored by John Langford, Principal Researcher at Microsoft Research, New York City. Vowpal Wabbit is an open source machine learning (ML) system sponsored by Microsoft. VW is the essence of speed in machine learning, able to learn from terafeature datasets with ease. Via parallel learning, it can exceed the throughput of… Read more