diff --git a/program/cohort.ipynb b/program/cohort.ipynb
index 6653147..f3843dc 100644
--- a/program/cohort.ipynb
+++ b/program/cohort.ipynb
@@ -4810,7 +4810,7 @@
     "\n",
     "-   <span style=\"padding:4px; background-color: #f2a68a; color: #000;\"><strong>Assignment 4.4</strong></span> SageMaker supports auto scaling your models. Auto scaling dynamically adjusts the number of instances provisioned for a model in response to changes in the workload. For this assignment, define a target-tracking scaling policy for a variant of your Endpoint and use the `SageMakerVariantInvocationsPerInstance` metric. `SageMakerVariantInvocationsPerInstance` is the average number of times per minute that the variant is invoked. Check [Automatically Scale Amazon SageMaker Models](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html) for more information about auto scaling models.\n",
     "\n",
-    "-   <span style=\"padding:4px; background-color: #f2a68a; color: #000;\"><strong>Assignment 4.5</strong></span> Modify the SageMaker Pipeline you created for the \"Pipeline of Digits\" project and add a Lambda Step to deploy the model automatically. Create a custom inference script so the endpoint receives a JSON containing the URL of an image, and returns a single value representing the predicted digit.\n"
+    "-   <span style=\"padding:4px; background-color: #f2a68a; color: #000;\"><strong>Assignment 4.5</strong></span> TBD\n"
    ]
   },
   {