Introduction
5G Advanced’s highlighting feature is its more aggressive embrace of artificial intelligence (AI) in the network. This is particularly important as more advanced tasks using AI mechanisms are already underway—AI development services and generative AI services are becoming more popular every day.
For example, machine learning has revolutionized the training of network data analytics functions (NWDAF) in the core network (CN). As we advance towards 6G, we foresee the network transforming into an AI-centric platform that integrates AI internally and offers AI capabilities to empower various applications. This concept is called Artificial Intelligence as a Service (AIaaS).
AIaaS regards the network as a warehouse of AI models, data, algorithms, and software that can be used through well-defined and efficient APIs. The most notable aspect of AIaaS is that it supports incorporating AI into applications without developers and companies having to create extensive AI systems and solutions for a particular application. This changes the network from a thing to creating an ecosystem that fosters high-impact applications in diverse fields.
In this blog, we’ll examine the role of AIaaS in influencing the development of future 6G networks. We’ll begin with a concise overview of AI as a Service and its functionality.
A Primer on AI as a Service (AIaaS)
Before we start, it might be helpful to mention that before proceeding to the AIaaS concept, one of the necessary aspects that deserve detailed attention is machine learning operations (MLOps). MLOps are the processes and tools that allow for developing, maintaining, and monitoring a machine learning system. Usually, communications service providers (CSPs) activate MLOps using various means within the CSP that can be utilized across the different domains of the network.
These tools can be exposed to the Radio Access Network (RAN) and used to train AI-based network functions. Furthermore, within such a platform, the MLOps toolkit is available to applications that are deployed within the CSP infrastructure and is termed as MLOps-as-a-Service (MLOps-aaS).
AIaaS is a superset of MLOps-aaS, integrating all the MLOps-aaS APIs with more APIs. The new APIs integrate well with the network and help implement the concept of a Mobile Network as a Platform. The AIaaS APIs are fully formed through the combination of MLOps tools and toolsets in network functions such as RAN, Core Network (CN), and Management.
The AIaaS architecture also provides interactivity support, including but not limited to authentication and access management, in a way that it could construct and deliver more advanced artificial intelligence services to applications. Keep reading to understand the generic aspects of AI as a service.
Essential Aspects of AI as a Service (AIaaS)
As it pertains to the MLOps-aaS environment, the MLOps API suite is surely packed with the necessary components to ensure good management of ML models in terms of health, performance, and lifetime. This entails the provision of skills and resources such as training, deploying, and monitoring models in the network environment, which are central to the functions of AI development services.
The network data API suite allows applications to access various network presence and analytical data, including mobility events, network load, performance statistics, and operational energy usage. These can be very useful for generative AI development services.
To the customer models API suite, customer-specific models are created and managed throughout the entire development process—from building them with the sum of network and application data to providing interfaces for using the models. This adds significant value to AI development services.
As stated above, there are QoS APIs into which any API family may be extended. For example, an application’s AI/ML model inference step may have a latency constraint. The application may then try to deploy the model inside a container, use the network services API suite to provision computing resources to run it, and leverage the QoS API to ensure the required latency is met. This functionality enables the delivery of more advanced generative AI development services by optimizing the model.
Customizable APIs in AIaaS
The application of AI as a Service requires highly customizable APIs. API management is essential in content service delivery since the service integration of various network APIs is necessary to provide AI as a service. We imagine this to be self-service, where application requests trigger actions. This degree of orchestration is necessary for AI and generative AI development services to realize their benefits in current network environments.
Conclusion
The emergence of AI in the era of 5G Advanced facilitates the transformation of the 6G networks into AI-native platforms through AI development services. Managed networking, or AI as a service (AIaaS), allows the network to deliver all necessary and ready-made components for AI applications, including models, datasets, and tools, through simple application programming interfaces. This eliminates the complexity of deploying such applications.
As CSPs provide data privacy, mobility information, and quality of service, AIaaS empowers further diversification and opens new ranges of generative AI development services.
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