Machine Learning and Data Governance in Telecom Industry

Abstract: 

Network congestion has remained an ever-increasing problem. Operators have attempted a variety of strategies to match the network demand capacity with existing infrastructure, as the cost of deploying additional network capacities is expensive. To keep the cost under control, operators apply control measures to attempt to allocate bandwidth fairly among users and throttle the bandwidth of users that consume excessive bandwidth. This approach had limited success. Alternatively, techniques that utilize extra bandwidth for quality of experience (QOE) efficiency by over-provisioning the network has proved to be ineffective and inefficient due to a lack of proper estimation.
The evolution of 5G networks would lead manufacturers and telecom operators to use high-data transfer rates, wide network coverage, low latency to build smart factories using automation, artificial intelligence, and the Internet of Things (IoT). The application of advanced data science and AI can provide better predictive insights to improve network capacity-planning accuracy. Better network provisioning would yield better network utilization for both next-generation networks based on 5G technology and current LTE and 4G networks. Further AI models can be designed to link application throughput with network performance, prompting users to plan their daily usage based on their current location and total monthly budget.

In this talk, we will understand the current challenges in the telecom industry, the need for an AIOPS platform, and the mission held by telecom operators, communication service providers across the world for designing such AI frameworks, platforms, and best practices. We will see how increasing operator collaborations are helping to create, deploy, and productionize AI platforms for different AI use-cases. We will study one industrial use-case (with code) based on real-time field research to predict network capacity. In this respect, we will investigate how deep learning networks can be used to train large volumes of data at scale (millions of network cells), and how its use can help the upcoming 5G networks. We will also examine an end-to-end pipeline of hosting the scalable framework on Google Cloud with special emphasis on Data Governance and Data Management. As data volume is huge and data needs to be stored in highly secured systems, we build our high-performing system with extra security features that can process millions of requests in an order of a few mili-secs. As the session highlights parameters and metrics in creating the neural network, it also discusses the challenges and some of the key aspects involved in designing and scaling the system.

Bio: 

Sharmistha is currently working as a Senior Manager at Publicis Sapient where she is working on different AI, machine learning, and data governance problems. She holds a Master Degree in Computer Science and Engineering from Aalto University. Her Master's thesis was done in collaboration with Nokia Research centre under the supervision of Professor Joerg Ott (https://www.netlab.tkk.fi/~jo/), who is also the co-chair of SIP working group and other telecommunication research organisations and startups in Europe.

Sharmistha has in-depth expertise in productionizing and scaling AI solutions on AWS and Google Cloud. She is a certified Professional Google Cloud Architect. Prior to joining Publicis Sapient she has worked at a startup called Datami Mobile Solutions, sprung from Princeton University. Here she has filed 5 patents (https://patents.justia.com/inventor/sharmistha-chatterjee). Her work in SIP has been cited by Microsoft patent (https://patents.google.com/patent/US8682889B2/en).

She holds expertise in both research and productionizing scalable AI solutions, and skillset to bridge the gap between theory and practice. She has a proven track record of delivering complex innovative solutions to fortune 500 companies increasing revenue to $5M (in early-stage start-ups: Princeton University start-up, Ittiam Systems, SAP labs) to larger than $500M. She has worked with direct stakeholders, the world’s most renowned, award-winning research scientists in the field of telecom, media, advertising, and IOT domain. She is also a medium blogger.