Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital
June Andrews is a Principal Data Scientist at Wise.io, From GE Digital working on a machine learning and data science platform for the Industrial Internet of Things, which includes aviation, trains, and power plants. Previously, she worked at Pinterest spearheading the Data Trustworthiness and Signals Program to create a healthy data ecosystem for machine learning. She has also lead efforts at LinkedIn on growth, engagement, and social network analysis to increase economic opportunity for professionals. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell.
Abstract Summary:
Counter Intuitive Machine Learning for the Industrial Internet of Things:
The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world’s most valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT the cost of being wrong can be the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale through the digitalization of industrial assets, there is clearly a growing role for machine learning to help augment and automate human decision making. It is against this backdrop that traditional machine learning techniques must be adapted and need based innovations created. We see industrial machine learning as distinct from consumer machine learning and in this talk we will cover the counterintuitive changes of featurization, metrics for model performance, and human-in-the-loop design changes for using machine learning in an industrial environment.
See Dr. June's slide here: https://www.slideshare.net/SessionsEvents/dr-june-andrews-principal-data-scientist-wiseio-from-ge-digital-at-mlconf-sf-2017
Видео Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital канала MLconf
Abstract Summary:
Counter Intuitive Machine Learning for the Industrial Internet of Things:
The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world’s most valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT the cost of being wrong can be the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale through the digitalization of industrial assets, there is clearly a growing role for machine learning to help augment and automate human decision making. It is against this backdrop that traditional machine learning techniques must be adapted and need based innovations created. We see industrial machine learning as distinct from consumer machine learning and in this talk we will cover the counterintuitive changes of featurization, metrics for model performance, and human-in-the-loop design changes for using machine learning in an industrial environment.
See Dr. June's slide here: https://www.slideshare.net/SessionsEvents/dr-june-andrews-principal-data-scientist-wiseio-from-ge-digital-at-mlconf-sf-2017
Видео Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital канала MLconf
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![Building Machine Learning Models with Strict Privacy Boundaries](https://i.ytimg.com/vi/HIKpXVc1mpo/default.jpg)
![Anima Anadkumar, Principal Scientist, Amazon Web Services, Endowed Professor, CalTech](https://i.ytimg.com/vi/RRy-3VXA0nw/default.jpg)
![Manipulating and Measuring Model Interpretability](https://i.ytimg.com/vi/hHAW1ug2qlE/default.jpg)
![Jennifer Marsman, Principal Developer Evangelist, Microsoft @ MLconf NYC](https://i.ytimg.com/vi/T8FaWkqzK0A/default.jpg)
![MLconf Online 2020: DevOps for Data Science With Kubernetes by Sophie Watson](https://i.ytimg.com/vi/9TqHilvnUuM/default.jpg)
![Sven Kreiss, Lead Data Scientist, Wildcard @ MLconf ATL](https://i.ytimg.com/vi/09kpP-w4DLI/default.jpg)
![Virginia Smith - A General Framework for Communication-Efficient Distributed... - MLconf SF 2016](https://i.ytimg.com/vi/vuGiNJoq8NQ/default.jpg)
![Jeremy Stanley, EVP/Data Scientist, Sailthru @ MLconf NYC](https://i.ytimg.com/vi/vEemVVLGo6E/default.jpg)
![Sanjeev Satheesh, The Story of End to End Models in Deep Learning at The AI Conference 2017](https://i.ytimg.com/vi/h3Y3Gohn1HI/default.jpg)
![MLconf Online 2020: Data Science is Key to Achieving Energy Access in Africa Madeleine Gleave](https://i.ytimg.com/vi/i9FXqOeFpwY/default.jpg)
![Subutai Ahmad, VP of Research, Numenta @ MLconf SF](https://i.ytimg.com/vi/SxtsCrTHz-4/default.jpg)
![Justin Basilico, Senior Researcher Engineer in Recommendation Systems, Netlix @ MLconf ATL](https://i.ytimg.com/vi/doWgbo-c9sM/default.jpg)
![Sergei Vassilvitskii, Research Scientist, Google @ MLconf NYC](https://i.ytimg.com/vi/rtXeauFFCE4/default.jpg)
![MLconf Online 2020: Mathematical Approaches to Clustering by Joseph Ross](https://i.ytimg.com/vi/ziZ2JfXDAd4/default.jpg)
![Byron Galbraith, Chief Data Scientist, Talla, NYC 2017](https://i.ytimg.com/vi/IHCtfiI8llA/default.jpg)
![MLconf NYC 2022: How to Detect and Interpret Data Drift in Production by Emeli Dral of Evidently AI](https://i.ytimg.com/vi/FnVi_-eq4yE/default.jpg)
![Optimized Image Classification on the Cheap](https://i.ytimg.com/vi/P5rU5LJfV5A/default.jpg)
![Carlos Guestrin, CEO of Dato Inc. @ MLconf SEA](https://i.ytimg.com/vi/gjSC5ZjLnII/default.jpg)
![Johann Schleier Smith, Co Founder and CTO, ifwe @ MLconf SF](https://i.ytimg.com/vi/t6eAdPof9yQ/default.jpg)
![Using a Bayesian Neural Network in the Detection of Exoplanets](https://i.ytimg.com/vi/u42czORKkt8/default.jpg)