Machine Learning

Detecting “Action” and “Cut” in Archival Footage Using a Multi-model Computer Vision and Audio Approach with Azure Cognitive Services

September 27, 2021 Sep 27, 2021 09/27/21

Nile Wilson

Movies and TV shows require multiple takes per scene and may have a substantial amount of archival footage as a result. Here, we use Azure Cognitive Services and custom code to develop a multi-model Machine Learning (ML) solution to automatically detect discardable footage to save media companies manual archiving hours and storage space.

Building an Action Detection Scoring Pipeline for Digital Dailies

September 20, 2021 Sep 20, 2021 09/20/21

Samuel Mendenhall

Media companies capture footage filmed for the entire day in what's known as ‘digital dailies’. When talking about terabytes and petabytes of content, storage costs can be a factor. Lets explore Machine Learning approaches to identify which content can be archived or discarded which will save on those storage costs.

Entity Disambiguation Using Search Engine

June 14, 2021 Jun 14, 2021 06/14/21

Maysam Mokarian

This blog post proposes a methodology to disambiguate misspelled entities by comparing the search retrieval performance with different custom search analyzers in a search engine.

Developing and Deploying a Recommender Model for Continuous Systematic Literature Reviews

February 10, 2021 Feb 10, 2021 02/10/21

Paolo Tenti

This blog post is about using the Microsoft Academic Graph and NLP to build a personalized recommender system to suggest new scientific publications to researchers maintaining Systematic Literature Reviews.

Evaluation Framework for Information Extraction

January 13, 2021 Jan 13, 2021 01/13/21

Omri Mendels

In this blog post we cover the process, requirements, and the design of an evaluation framework for NLP and Information Extraction. We cover the reasoning behind such a framework, and discuss its implementation with examples from a Named Entity Recognition evaluation point of view.

Building A Clinical Data Drift Monitoring System With Azure DevOps, Azure Databricks, And MLflow

October 29, 2020 Oct 29, 2020 10/29/20

Nile Wilson

Hospitals around the world regularly work towards improving the health of their patients as well as ensuring there are enough resources available for patients awaiting care. During these unprecedented times with the COVID-19 pandemic, Intensive Care Units are having to make difficult decisions at a greater frequency to optimize patient health ...

Preventing Rhino Poaching With Microsoft Azure

May 7, 2019 May 7, 2019 05/7/19

Olga Liakhovich

In the last eight years, poaching in Africa has been happening at an alarming rate. Currently, the continent loses three rhinos a day. As part of our team’s mission to find new ways that technology can positively impact our world, we’ve been collaborating with the Combating Wildlife Crime team Peace Parks Foundation (PPF), in partnership with

Assessing the Severity of Acne via Cell Phone Selfie Images Using A Deep Learning Model

February 5, 2019 Feb 5, 2019 02/5/19

Hang Zhang

Nestlé Skin Health partnered with Microsoft to develop a deep learning model powered mobile app able to assess acne severity using only uploaded selfie images as a source.

Developing and Deploying a Churn Prediction Model with Azure Machine Learning Services

January 10, 2019 Jan 10, 2019 01/10/19

Patty Ryan

Deep learning algorithms capable of learning and predicting customer behavior are allowing businesses to intervene with the right retention offers at the right time. CSE recently partnered with Majid Al Futtaim Ventures (MAF) to design and deploy a machine learning solution to predict attrition.

Real-Time Time Series Analysis at Scale for Trending Topics Detection

January 2, 2019 Jan 2, 2019 01/2/19

Omri Mendels

This code story describes a collaboration with ZenCity around detecting trending topics at scale. We discuss the datasets, data preparation, models used and the deployment story for this scenario.

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