The key aspect in solving ML problems in telecom industry lies in continuous data collection and evaluation from different categories of customers and networks so as to track and dive into varying performance metrics. The KPIs form the basis of network monitoring helping network/telecom operators to automatically add and scale network resources. Such smart automated systems are built with the objective of increasing customer engagement through enhanced customer experience and tracking customer behavior anomaly with timely detection and correction. Further the system is designed to scale and serve current LTE, 4G and upcoming 5G networks with minimal non-effective cell site visits and quick identification of Root Cause Analysis (RCA).

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 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 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 produce 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. 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 request in an order of few mili-secs. As the session highlights parameters and metrics in creating a LSTM based neural network, it also discusses the challenges and some of the key aspects involved in designing and scaling the system.