Machine Learning Model Deactivation

Description

Machine Learning (ML) model deactivation is a defined process within the 3GPP management framework that disables an ML model for a specific ML-enabled feature. In the context of 3GPP networks, ML models are increasingly used to optimize various aspects of network operations, such as mobility management, load balancing, and radio resource management. Deactivation is a critical lifecycle operation that allows the network to stop using a particular model instance while preserving its configuration and data for potential future reactivation. This is distinct from model deletion, which removes the model entirely.

The deactivation process is typically initiated by the network management system (e.g., via the Management Data Analytics Function, MDAF, or a similar entity) or by the ML model owner. It involves signaling to the relevant network functions (NFs) that the model should no longer be used for inference. The NF then stops invoking the model and may fall back to a default or legacy algorithm. The model's state is updated to 'deactivated', and it is no longer considered active for the associated feature. This process ensures that the network can gracefully transition away from an ML-based solution without disrupting ongoing services.

Key components involved in ML model deactivation include the ML model itself, the ML-enabled feature (e.g., network slicing management, QoS prediction), the ML model management function (e.g., in the Management Data Analytics Service, MDAS), and the consumer NFs. The deactivation may be triggered by various events, such as performance degradation, security concerns, or the need to update the model. The process must be standardized to ensure interoperability between different vendors' ML models and management systems.

In terms of architecture, ML model deactivation is part of the broader ML model lifecycle management, which is being standardized in 3GPP Release 19. The lifecycle includes training, testing, deployment, inference, monitoring, maintenance, and deactivation. Deactivation is often a precursor to model replacement or retirement. It allows operators to maintain control over their ML assets and ensures that models can be taken out of service without permanent loss. This is particularly important in multi-vendor environments where models may be provided by different vendors and need to be managed consistently.

The role of ML model deactivation in the network is to provide a safe and controlled mechanism for disabling ML functionality. It helps in managing the risk associated with ML models, such as unexpected behavior or performance issues. By deactivating a model, the network can revert to deterministic algorithms, ensuring service continuity. This capability is essential for building trust in AI/ML-based network automation and is a prerequisite for large-scale deployment of ML in 5G-Advanced and 6G networks.

Purpose & Motivation

ML model deactivation exists to provide a standardized, safe, and reversible way to disable an ML model for a specific feature without deleting it. As 3GPP networks increasingly adopt AI/ML for network optimization, the need for robust lifecycle management becomes paramount. Without a deactivation mechanism, operators would have to either leave a problematic model active or delete it entirely, losing the ability to quickly revert or reuse it. Deactivation solves this by allowing a model to be temporarily taken out of service, enabling fallback to legacy algorithms and facilitating troubleshooting.

The motivation for defining this term in Release 19 stems from the growing complexity of ML-based features and the need for multi-vendor interoperability. Previous releases introduced ML model management concepts, but the lifecycle was not fully specified. Deactivation is a critical piece that ensures operators can manage ML models throughout their lifecycle, from deployment to retirement. It also addresses regulatory and security requirements, as deactivation can be used to quickly disable a model if it is found to be non-compliant or compromised.

Historically, network functions were deterministic and did not require such lifecycle operations. With the introduction of AI/ML, the behavior of network functions can change over time, and models may need to be updated or disabled. The deactivation process provides a controlled way to handle these changes, reducing the risk of service disruption. It is a foundational element for autonomous networks, where ML models are expected to be managed dynamically.