Applied Machine Learning for Anomaly Detection on Equipment
09:00-16:30 January 26

Full day
Intermediate level

@ Foyer Garden 2

Details

Equipment failure in heavy industry (maritime, oil and gas, mining...) accounts for millions of dollars in downtime and repairs annually. In this workshop, we will go through a use case of detecting non-normal equipment behavior using sensor data. You will be training models on historic data and have to deploy these models into a cloud environment to make predictions on live stream data.


Outcome

This is an opportunity to familiarize yourself with end-to-end unsupervised Machine Learning in a production environment. You will also have a chance to productionize your trained Machine Learning model by deploying it into the cloud.


Prerequisites

  • Intermediate level in ML and data science
  • Working knowledge in open source Python Machine Learning stack is preferred, but R and Matlab users welcome
  • No business knowledge or expertise required
  • Only Python can be deployed to the cloud

Organizers

Alexandra Gunderson

Data Scientist, Arundo Analytics

Trung Doan

Data scientist, Arundo Analytics

Lukasz Mentel

Data scientist, Arundo Analytics