Here’s a quick guide to the 2024-25 FCS National Championship Game, including the schedule, TV network and how teams are selected.
CHAMPIONSHIP UPDATES: Live updates for the 2024 FCS Playoffs from selections to the championship
When is the 2024-25 FCS National Championship Game?
The 2024-25 FCS Championship is on Monday, January 6, 202
How can the status of a newly created SageMaker endpoint be checked after initiating its deployment?
This code snippet demonstrates how to deploy a custom machine learning (ML) model as a SageMaker endpoint using the AWS SDK for Python (Boto3).
Here’s a breakdown:
* **`endpoint_name = f”ai-vad-model-endpoint-{current_datetime}”`**: This line generates a unique name for your SageMaker endpoint. It combines the prefix “ai-vad-model-endpoint-” with the current date and time. This ensures that each endpoint has a distinct name, avoiding conflicts.
* **`sagemaker_client.create_endpoint(EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name)`**: This is the core line that creates the endpoint. It uses the `create_endpoint` method of the SageMaker client object (`sagemaker_client`).
* **`EndpointName`**: This parameter takes the unique endpoint name you generated earlier.
* **`EndpointConfigName`**: This parameter refers to the name of a previously configured endpoint configuration. An endpoint configuration defines the resources and settings for your endpoint, such as the model, instance type, and scaling behavior.
* **`Please note that it might take a few minutes till the status of the endpoint changes from “Creating” to “InService”. The current status can be checked as shown below.`**: This part highlights that deploying an endpoint takes time.
The provided snippet doesn’t include the code for checking the endpoint status, but it mentions that it can be done.
[[1](https://towardsdatascience.com/deploy-a-custom-ml-model-as-a-sagemaker-endpoint-6d2540226428)]
