Nokia launches the first AI use case library on public cloud for CSPs

Nokia AI as a serviceNokia, in collaboration with Microsoft, today announced the world’s first deployment of multiple AI use cases delivered over public cloud.

Nokia AVA AI as a service integrates Nokia’s security framework with Microsoft Azure’s digital architecture, allowing communications service  providers (CSPs) to securely inject AI into their networks nine times faster than using private cloud and scale fast across their network.

AI use cases are essential for CSPs to manage the business complexity that 5G and cloud networks bring, and will help accelerate digital transformation, Nokia said. The AI as a service enables faster deployment while also eliminating the concerns around data sovereignty and security.

After the initial data set-up, CSPs can deploy additional AVA AI use cases within one week and ramp-up or ramp-down resources as needed within one day across multiple network clusters.

The Nokia security framework on Azure ensures data is segregated and isolated to provide the same level of security as a private cloud.

Australian mobile operator TPG was the first commercial adopter of Nokia AVA AI on public cloud, using a local instance of Microsoft Azure. This means TPG can deploy and scale additional AI use cases fast and has been able to optimize network coverage, capacity and performance.

Some of the capabilities include the following:

  • Detecting network anomalies with great accuracy.
  • Reducing radio frequency optimization cycle times by 50%, allowing them to be performed more frequently and at lower cost.
  • Decreasing CO2 emissions by eliminating drive-testing.

Friedrich Trawoeger, Vice President, Cloud and Cognitive Services, Nokia said: “CSPs are under constant pressure to reduce costs by automating business processes through AI and machine learning. To meet market demands, telcos are turning to us for Telco AI-as-a-Service and this launch represents an important milestone in our multi-cloud strategy.”

“Operators can achieve significantly faster implementation times and can access a library of AI use cases remotely to improve network performance, lower costs, and reduce environmental impact at the same time,” Trawoeger added.