The telco world is awash with data. Floods of it. This won’t come as any surprise to Data Managers or IT executives working for Communications Service Providers (CSPs). Many are already struggling to manage and make sense of the rapidly rising variety, velocity and volume of data sweeping across their networks.  

There is a huge need to utilize this data for different AI use cases. Yet, extracting and preparing the data is a major headache, especially when you consider the chronic shortage of skilled data scientists.

Advanced and automated data analytics are the only way to meet the challenge.

From Data lakes to Data Mesh

For CSPs to develop their own AI use cases, they need a data framework that not only addresses the size and complexity of telco data but ensures real-time data gathering, fragmented data handling, distributed data computation, and the convergence of data from multiple locations and business units.

The conventional approach is to deploy data lakes that store information centrally. While data lakes work well with batch-oriented workloads, their monolithic storage is not well suited to low latency, highly demanding, real-time business applications. Furthermore, data lakes are hard to scale. Some CSPs add resources to stretch legacy data capacity as demand grows, but eventually, a breaking point will be reached with a backlogged data team struggling to keep pace with the demands of the business.

A more modern and agile approach is the data mesh which enables greater autonomy and flexibility for data owners and consumers. Unlike data lakes that manage data in one central place, a data mesh supports distributed, domain-specific data consumers. One of the key elements of the data mesh framework is data products - standardized, ready-to-use data sets that provide valuable data in a structured and efficient manner.  

For deploying AI use cases at scale, a data mesh alone is not enough. To fully leverage its potential, CSPs must embrace MLOps—the marriage of Machine Learning and Operations. MLOps provides the approach for deploying, managing, and optimizing machine learning models at scale. By automating model deployment and continuous monitoring, MLOps ensures that AI/ML algorithms deliver real-time insights to support critical business functions. Furthermore, LLMOps - a subset of MLOps tools, practices, and processes tailored to large language models’, is just a crucial component for deploying and building Large Language Models successfully.

Data mesh & MLOps - faster AI innovation

The data mesh approach underpins Nokia AVA Data Suite.  Nokia AVA Data Suite offers ready-to-use data products that are tailored to fasten insights and accelerate the MLOps process– so that CSPs can focus on building their own AI use cases with ease.  

The data products are formed by ingesting and correlating data from diverse sources across multi-vendor, multi-domain networks. AVA Data Suite also provides data governance and security mechanisms across the data product lifecycle to validate, assure, and maintain accurate data availability, synchronization, reliability, and relevance. The decentralized, scalable, and efficient nature of data mesh architecture, along with its emphasis on data quality and governance, makes it an important component for the success of LLMOps as well. With AVA Data Suite’s re-usable data products, the AI/ML lifecycle can be around 70% faster time to value than in a typical project.

Adding the MLOps approach with AVA Data Suite’s data products ensures a quick development and deployment of AI use cases. Nokia’s Advanced Consulting Services has extensive industry knowledge, having helped many CSPs around the world to prioritize, build and scale AI use cases underpinned by a robust set of MLOps practices.

By embracing a decentralized approach to data management and leveraging MLOps capabilities, CSPs can unlock the full potential of their data, drive AI innovation, and gain a competitive edge in the market.

Resources

Discover more about Nokia’s data mesh approach and the AVA Data Suite here.

Read more about our GenAI strategy here.