README.md

@@ -93,7 +93,7 @@ mlflow:
   tracking_server_name: your-tracking-server-name
 ```
 
-When MLflow is enabled, `train start` creates an MLflow run for the SageMaker job. `train status` finalizes that run once the job reaches a terminal state and registers completed model artifacts as pre-release model versions using the `prerelease-latest` MLflow alias.
+When MLflow is enabled, `train start` creates an MLflow run for the SageMaker job. `train status` finalizes that run once the job reaches a terminal state and registers completed model artifacts as experiment model versions using the `experiment-latest` MLflow alias. An experiment version is an immutable trained-source artifact; it records that training produced a model, not that the model is better than earlier versions or ready for release.
 
 To open the managed SageMaker MLflow UI, request a fresh presigned URL:
 
@@ -155,6 +155,46 @@ qc-cli train list --limit 3     Show a custom number of recent jobs
 
 The expected output artifact is SageMaker’s `model.tar.gz`, normally containing the trained model file your container writes to `/opt/ml/model`.
 
+## Model lifecycle
+
+The CLI uses neutral experiment naming for trained artifacts and reserves release terminology for an explicit promotion step.
+
+Current behavior:
+
+1. `qc-cli train start` submits a SageMaker training job.
+2. `qc-cli train status` finalizes the MLflow run after the job reaches a terminal state.
+3. If the job completed and `mlflow.register_trained_models` is enabled, the SageMaker `model.tar.gz` is registered as a new MLflow model version with:
+   - `qc_cli.stage=experiment`
+   - `qc_cli.artifact_kind=trained_source`
+   - `qc_cli.source=sagemaker`
+4. The MLflow alias `experiment-latest` points at the most recently registered experiment version.
+
+Planned AI Hub extension:
+
+1. AI Hub compile or quantize will create deployable derived artifacts from a trained-source experiment.
+2. Derived artifacts will keep lineage back to the source experiment version instead of replacing it.
+3. Release aliases such as `v1` or `production` will point at the selected deployable artifact.
+
+Example future metadata:
+
+```text
+qc-cli-model version 12
+qc_cli.stage=experiment
+qc_cli.artifact_kind=trained_source
+qc_cli.source=sagemaker
+
+qc-cli-model-aihub version 3
+qc_cli.stage=ai_hub_compiled
+qc_cli.artifact_kind=deployable
+qc_cli.parent_registered_model_name=qc-cli-model
+qc_cli.parent_model_version=12
+qc_cli.runtime=tflite
+qc_cli.quantization=int8
+qc_cli.target_device=Samsung Galaxy S25
+```
+
+In that flow, `experiment-latest` remains a training convenience alias. Release selection is a separate promotion decision based on the derived artifact, not on the experiment name.
+
 ## AWS permissions required
 
 The IAM user or role running the CLI needs:

src/commands/train.py

@@ -148,8 +148,8 @@ def status(
             updates["registered_model_version"] = version
         st.update_training_job(job_name, **updates)
         if version:
-            st.set_latest_prerelease_model_version(version)
+            st.set_latest_experiment_model_version(version)
-            CONSOLE.print(f"MLflow model version: [cyan]{version}[/cyan] ([cyan]prerelease-latest[/cyan])")
+            CONSOLE.print(f"MLflow model version: [cyan]{version}[/cyan] ([cyan]experiment-latest[/cyan])")
     if run_id and cfg.mlflow.mode is not MlflowMode.disabled:
         CONSOLE.print("Open MLflow: [cyan]qc-cli infra mlflow-url[/cyan]")
 

src/state.py

@@ -48,8 +48,8 @@ class CliStateStore:
         state["training_jobs"] = jobs
         self._write(state)
 
-    def set_latest_prerelease_model_version(self, version: str) -> None:
+    def set_latest_experiment_model_version(self, version: str) -> None:
-        self.update(latest_prerelease_model_version=version)
+        self.update(latest_experiment_model_version=version)
 
     def _write(self, state: dict[str, Any]) -> None:
         with open(self.path, "w") as f:

src/tracking/mlflow.py

@@ -78,7 +78,9 @@ class MlflowTracker:
         self._log_params({f"hyperparameters.{key}": value for key, value in training_job.hyperparameters.items()})
         mlflow.set_tags(
             {
-                "qc_cli.stage": "prerelease",
+                "qc_cli.stage": "experiment",
+                "qc_cli.artifact_kind": "trained_source",
+                "qc_cli.source": "sagemaker",
                 "qc_cli.command": "train start",
                 "sagemaker.job_name": training_job.job_name,
             }
@@ -117,12 +119,14 @@ class MlflowTracker:
                 source=training_job_status.model_artifacts,
                 run_id=run_id,
                 tags={
-                    "qc_cli.stage": "prerelease",
+                    "qc_cli.stage": "experiment",
+                    "qc_cli.artifact_kind": "trained_source",
+                    "qc_cli.source": "sagemaker",
                     "sagemaker.job_name": training_job_status.name,
                 },
             )
             version_number = str(version.version)
-            client.set_registered_model_alias(self.registered_model_name, "prerelease-latest", version_number)
+            client.set_registered_model_alias(self.registered_model_name, "experiment-latest", version_number)
             mlflow.set_tag("qc_cli.registered_model_name", self.registered_model_name)
             mlflow.set_tag("qc_cli.registered_model_version", version_number)
             return version_number