diff --git a/tutorials/basic-usage-of-jupyter-mlflow-and-prefect.md b/tutorials/basic-usage-of-jupyter-mlflow-and-prefect.md
index 4f5ca03f..646d4e3f 100644
--- a/tutorials/basic-usage-of-jupyter-mlflow-and-prefect.md
+++ b/tutorials/basic-usage-of-jupyter-mlflow-and-prefect.md
@@ -155,7 +155,7 @@ Now train the model twice more - first with the default parameters, and then wit
 
 ```python
 train_model(data, mlflow_experiment_id=1, alpha=0.3, l1_ratio=0.3)
-train_model(data, mlflow_experiment_id=1, alpha=0.3, l1_ratio=0.3)
+train_model(data, mlflow_experiment_id=1, alpha=0.5, l1_ratio=0.5)
 ```
 
 ![](./images/mlflow-results.png)
diff --git a/tutorials/deploy-model-seldon.md b/tutorials/deploy-model-seldon.md
index 7e0b8371..9b7bd5c0 100644
--- a/tutorials/deploy-model-seldon.md
+++ b/tutorials/deploy-model-seldon.md
@@ -230,7 +230,8 @@ Note that we leave the `model_uri` as an argument, so that we can specify this e
 
 ```python
 @task
-def deploy_model(model_uri: str, namespace: str = "seldon"): logger = prefect.context.get("logger")
+def deploy_model(model_uri: str, namespace: str = "seldon"):
+    logger = prefect.context.get("logger")
 
     logger.info(f"Deploying model {model_uri} to enviroment {namespace}")
 
@@ -302,6 +303,7 @@ api_request = requests.post(
         headers={"Authorization": f"Bearer {token}"},
         timeout=600
 )
+```
 
 ## Deploying the model via the Prefect UI