Telecom AI: Canonical's guide for data teams on MLOps

Data is the new oil, and Artificial Intelligence is the way to monetize it. According to an IDC report, Artificial Intelligence (AI), alongside 5G, IoT, and cloud computing, is one of the technologies reshaping the telecom industry. From data-driven decisions to fully automated and self-healing networks, AI developments are accelerating innovation and driving costs of operation down. 

However, while it is easier than ever to implement AI solutions in the telecom space, navigating a landscape of multiple databases, workflow engines, and ML frameworks remains difficult. This whitepaper aims to provide a guide of existing use cases for AI/ML in mobile networks. It addresses core network, radio network, and enterprise IT parts, providing for each use case a list of questions that need to be answered before implementation. It also provides a recommendation of open-source software components required to build efficient solutions. All tooling choices are showcasing an option based on the most common needs and can be easily modified thanks to its open nature and modularity. 

What is MLOps? 

MLOps is the short term for machine learning operations, and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production at a large scale. 

MLOps is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring. It will become a standard as artificial intelligence is moving towards becoming part of the everyday business rather than just an innovative activity. 

Learn more about AI/ML 

• Our telco whitepaper aims to provide a guide of existing use cases for AI/ML in mobile networks. Download to discover: 
• Key questions to consider around core network, radio network, and enterprise IT parts before implementation, by use case 
• Recommendations of open-source software components for building efficient solutions 
• Operation tips for AI in production

To find out more, please visit the link below and download the whitepaper.