Using NWDAF and ML Operations (MLOps) to maximize the value of your 5G telecom networks
Macro trends like network disaggregation, software defined networks (SDN), cloud technology adoption, and emerging open specifications are driving major changes in the telecom value chain. These trends are taking place in today’s complex, 5G, multi-vendor ecosystems and offer opportunities for those prepared to take advantage of these changes.
The Network Data Analytics Function (NWDAF), as defined by 3GPP, brings much-needed standardization in complex 5G networks, providing guidance for how data analytics can be derived from the industry’s core and radio networks. These analytics and insights can improve customer experience and operational efficiency for telecom providers to drive real business results.
With 5G’s exponential data growth, only intelligent automation can extract the full potential and value from this wealth of network data. Leveraging machine learning operations (MLOps) and automation in conjunction with NWDAF allows organizations to proactively manage network performance, security, and user experience.
In this ebook, we take a look at the following:
- Why MLOps should precede NWDAF implementation
- Five things to keep in mind for NWDAF implementation
- A reference implementation of the data analytics function from Elastic
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