Machine Learning

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

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

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

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

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

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

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

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

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

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

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

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

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

Olga Liakhovich May 7, 2019 May 7, 2019 05/7/19

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

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

Background Watch this episode of the Decoded Show for an in-depth look with Gary Bradski, the inventor of OpenCV, and the Nestlé team. In the Global Burden of Disease Study 2015, Acne was identified as the 8th most common disease in the world and is estimated to affect as many as 700 million people globally. This skin health ...

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

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

Business Problem For a subscription service business, there are two ways to drive growth: grow the number of new customers, or increase the lifetime value from the customers that you already have by retaining more of them. Improving customer retention requires the ability to predict which subscribers are likely to cancel (referred to as churn...

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

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

Managing big cities and providing citizens public services requires municipalities to have a keen understanding of what citizens care the most about. And, as the cities we live in seek more and more to become the 'smart cities' of tomorrow, this means gathering and analyzing vast amounts of data. Data gathered from social and municipal sources...

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