- Add create_mlflow_run and log_evaluation_to_mlflow KFP components - Log accuracy, correct/total counts, pass/fail to MLflow experiment - Upload evaluation_results.json as artifact - Wire MLflow run into pipeline DAG before NATS publish
- voice_pipeline: STT → RAG → LLM → TTS - document_ingestion_pipeline: Extract → Chunk → Embed → Milvus - document_ingestion_mlflow_pipeline: With MLflow tracking - evaluation_pipeline: Model benchmarking - kfp-sync-job: K8s job to sync pipelines