From 8ecb8854baf7769c33d566867c88602201d888d5 Mon Sep 17 00:00:00 2001 From: "Santiago L. Valdarrama" Date: Mon, 23 Oct 2023 16:19:28 -0400 Subject: [PATCH] ... --- program/cohort.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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", "- Assignment 4.4 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", - "- Assignment 4.5 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" + "- Assignment 4.5 TBD\n" ] }, {