Machine Learning (ML)

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.

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 ...

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.

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.

Sequence Intent Classification Using Hierarchical Attention Networks

March 6, 2018 Mar 6, 2018 03/6/18

Olga Liakhovich

We analyze how Hierarchical Attention Neural Networks could be helpful with malware detection and classification scenarios, demonstrating the usefulness of this approach for generic sequence intent analysis. The novelty of our approach is in applying techniques that are used to discover structure in a narrative text to data that describes the behavior of executables.

Comparing Image-Classification Systems: Custom Vision Service vs. Inception

December 5, 2017 Dec 5, 2017 12/5/17

Clemens Wolff

This story covers how to get started with transfer-learning and build image classification models in Python with the Custom Vision Service. We compare the results with the popular Tensorflow-based models Inception and MobileNet.

Stock Market Predictions with Natural Language Deep Learning

December 4, 2017 Dec 4, 2017 12/4/17

Patty Ryan

We developed a deep learning model using a one-dimensional convolutional neural network to predict future stock market performance of companies using Azure Machine Learning Workbench and Keras.

Autoscaling Deep Learning Training with Kubernetes

November 21, 2017 Nov 21, 2017 11/21/17

Rita Zhang

We explore how we worked with a customer to add autoscaling capability to a Kubernetes cluster to meet bursty demands for deep learning training in a cost-efficient manner.

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