Managing Machine Learning Experiments at Scale
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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.
The best Amazon machine learning service to help manage the team's experiments at scale would be Amazon SageMaker's model experiments capability (Option C).
Amazon SageMaker is a managed machine learning service that enables developers to build, train, and deploy machine learning models quickly and easily. SageMaker provides a comprehensive set of tools for building, training, and deploying machine learning models, including built-in algorithms and frameworks, a model hosting environment, and tools for monitoring and managing models.
Option A, using Amazon SageMaker Inference Pipeline, is not the best option for managing the team's experiments since it is primarily designed to help manage the deployment of models, rather than the experiments themselves. Inference Pipeline is used to simplify the process of deploying multiple models into a single endpoint.
Option B, using Amazon SageMaker model tracking capability, is a useful feature for tracking individual models and their associated metadata, such as hyperparameters and training data. It can help identify the best-performing models and provide insights into the model's training process. However, this feature doesn't provide the team with a systematic way to manage experiments at scale.
Option D, using Amazon SageMaker model containers capability, is a lower-level feature that enables developers to build and deploy custom machine learning containers for SageMaker. While this feature provides additional flexibility, it doesn't directly help with managing experiments.
Option C, using Amazon SageMaker model experiments capability, is the best option for managing the team's experiments at scale. This feature enables teams to run and compare multiple experiments in a systematic and organized way. With this feature, teams can track and compare different training runs, save and manage metadata, and automate the training process to run multiple experiments in parallel. This capability makes it easier to manage the team's experiments, track the best-performing models, and optimize the model-building process.
