AWS Certified Machine Learning - Specialty Exam MLS-C01: Jupyter Notebook Settings for Testing and Evaluating Production Performance

Answer: D.

Option A is incorrect.

This is the type of EC2 instance on which your notebook will run.

This won't help you understand production performance.

Option B is incorrect.

The lifecycle configuration allows you to customize your notebook environment with default scripts and plugins.

Default jupyter notebook scripts and plugins won't give you an insight into production performance.

Option C is incorrect.

The volume size is just the size of the jupyter instance in GBs.

This won't give you an insight into production performance.

Option D is correct.

From the Amazon SageMaker developer guide titled Amazon SageMaker Elastic Inference (EI) “By using Amazon Elastic Inference (EI), you can speed up the throughput and decrease the latency of getting real-time inferences from your deep learning models … You can also add an EI accelerator to an Amazon SageMaker notebook instance so that you can test and evaluate inference performance when you are building your models”

Therefore, while you are in the development stage using jupyter notebooks, Elastic Inference allows you to gain insight into the production performance of your model once it is deployed.

Option E is incorrect.

From the Amazon SageMaker developer guide titled CreateModel “..

you name the model and describe a primary container.

For the primary container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.” So the primary container is a parameter used in the CreateModel request when you are creating a model in SageMaker.

It is not used when setting up your jupyter notebook.

Reference:

Please see the Amazon SageMaker developer guide titled Amazon SageMaker Elastic Inference (EI), the AWS FAQ titled Amazon Elastic Inference FAQs, and the AWS Machine Learning blog titled Optimizing costs in Amazon Elastic Inference with TensorFlow.

The setting that will allow you to test and evaluate production performance when building your models in the Jupyter notebook instance on SageMaker is D. Elastic inference.

Elastic Inference is an Amazon SageMaker feature that enables you to attach low-cost GPU-powered inference acceleration to Amazon EC2 and Amazon SageMaker instances. Elastic Inference helps reduce the cost of running deep learning inference workloads by up to 75% compared to using a full GPU instance. Elastic Inference allows you to attach the right amount of GPU-powered inference acceleration to your Amazon SageMaker notebook instances, training instances, or endpoint configurations, and pay only for what you need.

In the given scenario, since the requirement is to perform detections in real-time, Elastic Inference is the most appropriate choice. It allows you to optimize the inference performance of the Image Classification SageMaker built-in deep learning model by attaching the appropriate amount of GPU-powered inference acceleration to your Jupyter notebook instance. This will help you test and evaluate the production performance of your model in real-time.

Option A - Notebook instance type refers to the computing capacity of the instance, and choosing a powerful instance type may speed up the training process, but it does not affect the inference performance.

Option B - Lifecycle configuration is a way to customize the environment in which the notebook instance runs, but it does not affect the inference performance.

Option C - Volume size refers to the storage capacity of the notebook instance, but it does not affect the inference performance.

Option E - Primary container refers to the Docker container that runs the model, and it does not affect the inference performance directly.