AI Model Lifecycle Management

The Non-Real-Time RAN Intelligent Controller is the logical host for AI model lifecycle management. It provides the execution environment for rApps that train, validate, and deploy models with latency tolerances greater than 1 second. The Non-RT RIC communicates policy guidance and enrichment information to the Near-RT RIC over the A1 interface, serving as the central brain for long-term network optimization and model orchestration.

The automated pipeline within the Service Management and Orchestration (SMO) framework that manages the end-to-end model lifecycle. Key stages include:

• Data ingestion from the Data Collection and Distribution Framework
• Offline training using historical network telemetry
• Validation and testing against holdout datasets
• Packaging and deployment to inference hosts on the Near-RT RIC
• Continuous monitoring for model drift and performance degradation

A critical monitoring function that continuously compares the inference accuracy of a deployed AI model against a baseline performance metric. When the statistical properties of live network data diverge from the training distribution—due to changes in user behavior, topology, or interference patterns—drift detection triggers automated alerts. This enables model rollback to a previous stable version or initiates a new training cycle to restore optimization quality.

The standardized open interface connecting the Non-RT RIC to the Near-RT RIC. For model lifecycle management, the A1 interface carries:

• Policy-based guidance that dictates optimization targets
• Enrichment information such as predicted traffic patterns
• AI/ML model management messages including model deployment, activation, and rollback commands This interface ensures that models trained in the Non-RT RIC are safely and reliably pushed to the execution environment.

A coordination mechanism within the RAN Intelligent Controller that detects and resolves contradictory control commands from multiple concurrently running xApps. During A/B testing of new model versions, conflict mitigation ensures that experimental and production models do not issue opposing resource allocation decisions. This safeguard is essential for safe canary deployments and gradual model rollout strategies in live networks.

The infrastructure within the SMO that aggregates performance measurements, UE measurements, and network telemetry from O-RAN network functions via the O1 interface. This framework filters and distributes data streams to registered rApps for model training. High-quality, labeled data is the foundation of effective model lifecycle management—without it, even the most sophisticated AI models will fail to generalize.