AI/ML Workflow Orchestration

The orchestration pipeline begins with the Data Collection and Distribution Framework aggregating performance measurements from the O1 interface and UE/network telemetry from the E2 interface. Raw data is filtered, normalized, and transformed into training-ready datasets. This stage handles missing value imputation, feature engineering, and timestamp alignment across asynchronous data streams to ensure temporal consistency for time-series models.

Training jobs are executed on the Non-RT RIC platform using historical data from the R-NIB. The orchestrator manages hyperparameter tuning, cross-validation folds, and holdout set evaluation. Models are validated against operator-defined KPIs such as throughput gain or energy savings. The pipeline supports A/B testing by training candidate models in parallel and comparing their performance on identical data slices before promotion.

Validated models are serialized into portable formats (e.g., ONNX, PMML) and versioned with metadata including training data provenance, hyperparameters, and performance metrics. The AI Model Lifecycle Management component maintains a registry of all model versions, enabling rollback to a previous version if a newly deployed model underperforms. Each model artifact is cryptographically signed to ensure integrity during distribution.

The orchestrator pushes trained models to inference endpoints on the Near-RT RIC for execution by xApps. Deployment is governed by A1 policies that specify which models are authorized for which xApps and under what conditions. The pipeline supports canary deployments, routing a small percentage of traffic to a new model version while monitoring for anomalies before full rollout.

Once deployed, the orchestration framework continuously monitors inference accuracy against a baseline. Model Drift Detection compares live predictions with expected distributions and triggers alerts when degradation exceeds a threshold. Common causes include concept drift (changing network topology) and data drift (shifting traffic patterns). When drift is detected, the pipeline can automatically initiate retraining or fallback to a stable model version.

The orchestration pipeline implements Closed-Loop Automation by feeding production inference outcomes and new telemetry back into the training dataset. When performance degrades or new data becomes available, the pipeline automatically triggers a retraining cycle—repeating data ingestion, training, validation, and deployment—without human intervention. This ensures models adapt to evolving network conditions such as new cell deployments or seasonal traffic shifts.