You work for a fantasy sports wagering software company as a machine learning specialist. You are the leader of a team of machine learning specialists who have been given the assignment of building a model to predict the over/under line for every professional football game each week of the NFL season. Due to the complex nature of the problem and its many feature combinations, you have your team experimenting with different datasets, algorithms, and hyperparameters to find the best combination for your machine learning problem. You don’t want to limit the number of experiments your team can perform. Since you have a relatively large team of talented machine learning specialists, they will generate several hundred to over a thousand experiments over the course of your modeling effort.
Which Amazon machine learning service(s)/feature(s) should you use to help manage your team’s experiments at scale?

Answer options:

A.Use Amazon SageMaker Inference Pipeline.
B.Use Amazon SageMaker model tracking capability.
C.Use Amazon SageMaker model experiments capability.
D.Use Amazon SageMaker model containers capability.

Answer: B
Option A is incorrect. The Amazon Inference Pipeline is used to deploy pre-trained SageMaker algorithms packaged in Docker containers. You would not use Amazon Inference Pipeline to manage experiments at scale.
Option B is correct. You can use the Amazon SageMaker model tracking capability to search key model attributes such as hyperparameter values, the algorithm used, and tags associated with your team’s models. This SageMaker capability allows you to manage your team’s experiments at the scale of up to thousands of model experiments.
Option C is incorrect. There is no Amazon SageMaker feature called ‘model experiments capability’.
Option D is incorrect. There is no Amazon SageMaker feature called ‘model containers capability’.
Reference:
Please see the AWS announcement titled New Model Tracking Capabilities for Amazon SageMaker Are Now Generally Available, the Amazon SageMaker developer guide titled Manage Machine Learning Experiments, the AWS Machine Learning blog titled Using model attributes to track your training runs on Amazon SageMaker, the Amazon SageMaker developer guide titled Monitor and Analyze Training Jobs Using Metrics, and the Amazon SageMaker developer guide titled Deploy an Inference Pipeline.