Machine Learning

Improving Safety and Efficiency in BMW Manufacturing Plants with an Open Source Platform for Managing Inventory Delivery

December 19, 2018 Dec 19, 2018 12/19/18

Over the course of twelve months Microsoft and BMW partnered three different times to help BMW with its vision for technical transformation. An open-source package called ROS-Industrial was used to help provide the building blocks for the robotics work.

Active Learning for Object Detection in Partnership with Conservation Metrics

November 6, 2018 Nov 6, 2018 11/6/18

Olga Liakhovich

CSE teamed up with Conservation Metrics to use Active Learning to allow for more efficient data labeling for object detection projects.

Unsupervised driver safety estimation at scale, a collaboration with Pointer Telocation

July 30, 2018 Jul 30, 2018 07/30/18

Omri Mendels

A scalable unsupervised approach for driver safety estimation on Pointer Telocation's dataset

Semantic Segmentation of Small Data using Keras on an Azure Deep Learning Virtual Machine

July 18, 2018 Jul 18, 2018 07/18/18

Margaret Meehan

Golf performance tracking startup Arccos joined forces with Commercial Software Engineering (CSE) developers in hopes of unveiling new improvements to their "virtual caddie" this summer.

Deploying a Batch AI Cluster for Distributed Deep Learning Model Training

July 9, 2018 Jul 9, 2018 07/9/18

Stephanie Marker

Microsoft and Land O'Lakes partnered to develop an automated solution to identify sustainable farming practices given thousands of satellite images of Iowan farms. Our primary goal was to reduce the reliance on manual interviewing of farmers and make it more profitable for farmers to follow sustainable farming practices. To tackle this issue our team deployed a highly scalable Batch AI cluster on Azure and then performed distributed deep learning model training with Horovod.

Satellite Images Segmentation and Sustainable Farming

July 5, 2018 Jul 5, 2018 07/5/18

Olga Liakhovich

Can Machine Learning help with detecting sustainable farming practices? In this blog post inspired by our collaboration with Land O'Lakes we share the lessons we learned in the image segmentation space.

Using Otsu’s method to generate data for training of deep learning image segmentation models

May 17, 2018 May 17, 2018 05/17/18

Clemens Wolff

In this article, we introduce a technique to rapidly pre-label training data for image segmentation models such that annotators no longer have to painstakingly hand-annotate every pixel of interest in an image. The approach is implemented in Python and OpenCV and extensible to any image segmentation task that aims to identify a subset of visually distinct pixels in an image.

Fashion Retail Inventory Management With Deep Learning Content-based Image Retrieval

May 15, 2018 May 15, 2018 05/15/18

CY Yam

In early 2018, Microsoft partnered with a successful international online fashion retailer to develop an efficient system for logging newly arrived apparel items and quickly determining whether the item is already in stock

Making sense of Handwritten Sections in Scanned Documents using the Azure ML Package for Computer Vision and Azure Cognitive Services

May 7, 2018 May 7, 2018 05/7/18

Tingting Zhao

Extracting general concepts, rather than specific phrases, from documents and contracts is challenging. It's even more complicated when applied to scanned documents containing handwritten annotations. We describe using object detection and OCR with Azure ML Package for Computer Vision and Cognitive Services API.

Deep Learning Image Segmentation for Ecommerce Catalogue Visual Search

April 18, 2018 Apr 18, 2018 04/18/18

CY Yam

We explored possible techniques to identify duplicate catalogue entries in fashion retail through image similarity with deep learning and computer vision.

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