Operationalizing Streaming Telemetry and Machine Learning Model Serving: Customer Experience Automation (2020)

By Nick Pinckernell & Scott Rome, Comcast

Customer Experience, Telco, Machine Learning, Automation… all sounds a bit dry, no? If not, this should help shed light on a specific use case, but also provide details and lessons learned while building the solution. If so, read on to perhaps find some surprising details. Many are aware of the impact from Machine Learning but may not be aware just how many portions of the enterprise it is now altering -- or the magnitude of those changes.

With building an ML platform in mind, specifically to improve and automate the customer experience, this paper will illustrate how we accomplished this. It will also detail lessons learned and insights not only on the data itself, but on the architecture and thinking behind it.

Another focus is the challenges and differences of scaling and maintaining the Machine Learning components of the architecture. The highlight here is that while Proactive Network Management (PNM) has laid out a number of excellent methods on how to collect and analyze network telemetry from CPE and other headend equipment, it has not covered the aspects of what to do or how to handle the data with respect to utilizing ML models.

This paper will begin with the Customer Experience use case, and work from that point through to the end solution. A number of open source technologies are referenced as possible implementations for components. Other components can certainly be used.

The desire to share this information stems from the fact that some of these solutions are not easy. They require not only multiple resources to develop the front-end user interface, but the back-end platform as well. After the ML models have been trained, the task of building the platform, scaling, testing specific tools and gluing everything together is still rather manual and prone to performance and optimization challenges. With those things in mind, the details throughout this paper should aid the reader in determining directions to go when considering, building and scaling such a system.

By clicking the "Download Paper" button, you are agreeing to our terms and conditions.

Similar Papers

A Machine Learning Pipeline for D3.1 Profile Management
By Maher Harb, Jude Ferreira, Dan Rice, Bryan Santangelo & Rick Spanbauer, Comcast
2019
Optimizing Video Customer Experience with Machine Learning
By Mariela Fiorenzo, Claudio Righetti, María Cecilia Raggio, Fernando Ochoa & Gabriel Carro, Telecom Argentina S.A.
2019
Simplifying Field Operations Using Machine Learning
By Sanjay Dorairaj, Bernard Burg & Nicholas Pinckernell, Comcast Corporation; Chris Bastian, SCTE
2017
A PNM System Using Artificial Intelligence, HFC Network Impairment, Atmospheric and Weather Data to Predict HFC Network Degradation and Avert Customer Impact
By Larry Wolcott, Michael O'Dell, Peter Kuykendall, Vishnu Gopal, Jason Woodrich & Nick Pinckernell, Comcast
2018
Applications of Machine Learning in Cable Access Networks
By Karthik Sundaresan, Nicolas Metts, Greg White, Albert Cabellos-Aparicio, CableLabs
2016
Embracing Service Delivery Changes with Machine Learning
By Andrew Sundelin, Guavus, Inc.
2018
Improving the Customer Experience with Network Automation and with AI-Powered Voice
By Pravin Mahajan, Infinera
2018
Streaming Telemetry Data from the Home Network using OpenWrt Access CPE
By Shlomo Ovadia, Ph.D., Deependra Rawat & Dan Lynch, Charter Communications
2020
Using AI to Improve the Customer Experience: A Virtual Assistant Chatbot
By Bernard Burg, Fan Liu, Abel Villca Roque, Sunil Srinivasa, Ryan March & Tianwen Chen, Comcast
2018
Detecting Video Piracy with Machine Learning
By Matthew Tooley & Thomas Belford, NCTA – The Internet & Television Assocation
2019
More Results >>