feat: add MLflow tracking to evaluation pipeline

- 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

evaluation_pipeline.py

@@ -4,15 +4,98 @@ Model Evaluation Pipeline - Kubeflow Pipelines SDK
 
 Evaluates fine-tuned models against benchmarks.
 Integrates with Argo Workflows for automated model deployment.
+Logs evaluation results to MLflow for experiment tracking.
 
 Usage:
-  pip install kfp==2.12.1
+  pip install kfp==2.12.1 mlflow boto3 psycopg2-binary
   python evaluation_pipeline.py
 """
 
 from kfp import dsl
 from kfp import compiler
-from typing import Dict
+from typing import Dict, List, Any, NamedTuple
+
+
+# ---- MLflow KFP components (inline to avoid external dep) ----
+
+MLFLOW_IMAGE = "python:3.13-slim"
+MLFLOW_PACKAGES = ["mlflow>=2.10.0", "boto3", "psycopg2-binary"]
+
+
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
+def create_mlflow_run(
+    experiment_name: str,
+    run_name: str,
+    mlflow_tracking_uri: str = "http://mlflow.mlflow.svc.cluster.local:80",
+    tags: Dict[str, str] = None,
+    params: Dict[str, str] = None,
+) -> NamedTuple('RunInfo', [('run_id', str), ('experiment_id', str)]):
+    """Create an MLflow run and return the run_id."""
+    import os, mlflow
+    from mlflow.tracking import MlflowClient
+    from collections import namedtuple
+
+    mlflow.set_tracking_uri(mlflow_tracking_uri)
+    client = MlflowClient()
+
+    exp = client.get_experiment_by_name(experiment_name)
+    experiment_id = exp.experiment_id if exp else client.create_experiment(
+        name=experiment_name, artifact_location=f"/mlflow/artifacts/{experiment_name}"
+    )
+
+    default_tags = {
+        "pipeline.type": "evaluation",
+        "kfp.run_id": os.environ.get("KFP_RUN_ID", "unknown"),
+    }
+    if tags:
+        default_tags.update(tags)
+
+    run = mlflow.start_run(experiment_id=experiment_id, run_name=run_name, tags=default_tags)
+    if params:
+        mlflow.log_params(params)
+    run_id = run.info.run_id
+    mlflow.end_run()
+
+    RunInfo = namedtuple('RunInfo', ['run_id', 'experiment_id'])
+    return RunInfo(run_id, experiment_id)
+
+
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
+def log_evaluation_to_mlflow(
+    run_id: str,
+    model_name: str,
+    dataset_name: str,
+    metrics: dict,
+    results: list,
+    mlflow_tracking_uri: str = "http://mlflow.mlflow.svc.cluster.local:80",
+) -> str:
+    """Log evaluation metrics, params, and sample results to MLflow."""
+    import json, tempfile, mlflow
+    from mlflow.tracking import MlflowClient
+    from pathlib import Path
+
+    mlflow.set_tracking_uri(mlflow_tracking_uri)
+    client = MlflowClient()
+
+    client.log_param(run_id, "eval.model_name", model_name)
+    client.log_param(run_id, "eval.dataset", dataset_name)
+
+    for key, value in metrics.items():
+        client.log_metric(run_id, f"eval.{key}", float(value))
+
+    if results:
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = Path(tmpdir) / "evaluation_results.json"
+            path.write_text(json.dumps(results, indent=2))
+            client.log_artifact(run_id, str(path))
+
+    passed = metrics.get("pass", metrics.get("accuracy", 0) >= 0.7)
+    client.set_tag(run_id, "eval.passed", str(passed))
+    client.set_tag(run_id, "model.name", model_name)
+
+    # End the run
+    client.set_terminated(run_id, status="FINISHED")
+    return run_id
 
 
 @dsl.component(
@@ -175,6 +258,18 @@ def model_evaluation_pipeline(
         sample_limit: Maximum samples to evaluate
     """
 
+    # Create MLflow run for this evaluation
+    mlflow_run = create_mlflow_run(
+        experiment_name="model-evaluation",
+        run_name=f"eval-{model_name}-{dataset_name}",
+        params={
+            "model_endpoint": model_endpoint,
+            "model_name": model_name,
+            "dataset_name": dataset_name,
+            "sample_limit": str(sample_limit),
+        },
+    )
+
     # Load dataset
     load_task = load_eval_dataset(
         dataset_name=dataset_name,
@@ -193,11 +288,21 @@ def model_evaluation_pipeline(
     # Calculate metrics
     metrics_task = calculate_metrics(results=inference_task.output)
 
-    # Publish results
+    # Log results to MLflow
+    log_task = log_evaluation_to_mlflow(
+        run_id=mlflow_run.outputs["run_id"],
+        model_name=model_name,
+        dataset_name=dataset_name,
+        metrics=metrics_task.output,
+        results=inference_task.output,
+    )
+
+    # Publish results to NATS
     publish_task = publish_results(
         metrics=metrics_task.output,
         model_name=model_name
     )
+    publish_task.after(log_task)
 
 
 if __name__ == "__main__":