Given this scenario, which steps are MOST IMPORTANT for successfully deploying your custom container with SageMaker, ensuring that it meets the company’s requirements?

You are a machine learning engineer at a biotech company developing a custom deep learning model for analyzing genomic data. The model relies on a specific version of TensorFlow with custom Python libraries and dependencies that are not available in the standard SageMaker environments. To ensure compatibility and flexibility, you decide to use the "Bring Your Own Container" (BYOC) approach with Amazon SageMaker for both training and inference.

Given this scenario, which steps are MOST IMPORTANT for successfully deploying your custom container with SageMaker, ensuring that it meets the company’s requirements?
A . Package the model as a SageMaker-compatible file, upload it to Amazon S3, and use a pre-built SageMaker container for training, ensuring that the training job uses the custom environment
B . Create a Docker container with the required environment, push the container image to Amazon ECR (Elastic Container Registry), and use SageMaker’s Script Mode to execute the training script within the container
C . Build a Docker container with the required TensorFlow version and dependencies, push the
container image to Docker Hub, and reference the image in SageMaker when creating the training job
D . Deploy the model locally using Docker, then use the AWS Management Console to manually copy
the environment and model files to a SageMaker instance for training

View Answer

Answer: B

Explanation:

Correct option:

Create a Docker container with the required environment, push the container image to Amazon ECR (Elastic Container Registry), and use SageMaker’s Script Mode to execute the training script within the container

Script mode enables you to write custom training and inference code while still utilizing common ML framework containers maintained by AWS.

SageMaker supports most of the popular ML frameworks through pre-built containers, and has taken the extra step to optimize them to work especially well on AWS compute and network infrastructure in order to achieve near-linear scaling efficiency. These pre-built containers also provide some additional Python packages, such as Pandas and NumPy, so you can write your own code for training an algorithm. These frameworks also allow you to install any Python package hosted on PyPi by including a requirements.txt file with your training code or to include your own code directories.

This is the correct approach for using the BYOC strategy with SageMaker. You build a Docker container that includes the required TensorFlow version and custom dependencies, then push the image to Amazon ECR. SageMaker can reference this image to create training jobs and deploy endpoints. By using Script Mode, you can execute your custom training script within the container, ensuring compatibility with your specific environment.

via –

https://aws.amazon.com/blogs/machine-learning/bring-your-own-model-with-amazon-sagemaker-script-mode/

Incorrect options:

Build a Docker container with the required TensorFlow version and dependencies, push the container image to Docker Hub, and reference the image in SageMaker when creating the training job – While Docker Hub can be used to host container images, Amazon SageMaker is optimized to work with images stored in Amazon ECR, providing better security, performance, and integration with AWS services. Additionally, using Docker Hub for production ML workloads may pose security and compliance risks.

Package the model as a SageMaker-compatible file, upload it to Amazon S3, and use a pre-built

SageMaker container for training, ensuring that the training job uses the custom environment –

This option describes a standard SageMaker workflow using pre-built containers, which does not

provide the customization required by the BYOC approach. SageMaker pre-built containers may not support the specific custom libraries and dependencies your model requires.

Deploy the model locally using Docker, then use the AWS Management Console to manually copy the environment and model files to a SageMaker instance for training – Manually deploying the model and environment locally and then copying files to SageMaker instances is not scalable or maintainable. SageMaker BYOC allows for a more robust, automated, and integrated solution.

References:

https://aws.amazon.com/blogs/machine-learning/bring-your-own-model-with-amazon-sagemaker-script-mode/

https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers.html