You work for a large manufacturer of consumer electronic devices.
Your company wishes to build a machine learning model to predict which product has the most dedicated following among its consumer base.
This product will receive funding for future investment in new models and/or enhancements to existing models.
You and your machine learning team have a vast amount of observations of using the current product base.
You know you and your team need to perform feature engineering on the large dataset before using it to train your XGBoost algorithm-based model for predictions. What SageMaker feature can you use to perform the required feature engineering of your dataset in the most efficient way?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: C.
Option A is incorrect.
The SageMaker Automatic Model Tuning feature is used to automatically adjusting thousands of different combinations of hyperparameters to give you the most accurate predictions for your model.
But you are trying to perform feature engineering transformation prior to training.
So this option is not correct.
Option B is incorrect.
The Built-In Transforms feature is part of the AWS Glue service, not SageMaker.
Option C is correct.
The SageMaker Batch Transform feature can be used to preprocess your data before using the data in your training runs.
Option D is incorrect.
The SageMaker Hosting Services feature is used to allow your model to provide inferences once you've trained your model.
But you are trying to perform feature engineering transformation prior to training.
So this option is not correct.
Reference:
Please see the Amazon SageMaker developer guide titled Run Batch Transforms with Inference Pipelines, the Amazon SageMaker developer guide titled Get Inferences for an Entire Dataset with Batch Transform, the Amazon SageMaker Features overview page, the Amazon SageMaker developer guide titled Deploy a Model on Amazon SageMaker Hosting Services, and the AWS Glue developer guide titled Built-In Transforms.
The most appropriate SageMaker feature to use in this scenario would be Built-In Transforms.
Built-In Transforms is a SageMaker feature that allows you to perform data preprocessing and feature engineering on large datasets in an efficient and scalable way. With Built-In Transforms, you can easily apply transformations such as data cleaning, normalization, and encoding to your dataset before training your machine learning model.
In the case of this scenario, before training the XGBoost algorithm-based model, the dataset needs to undergo feature engineering to extract relevant features that could be used as input to the model. For example, the dataset may contain features such as product ratings, customer reviews, purchase history, and demographic information. These features need to be processed and transformed to extract valuable insights that could be used to build an accurate prediction model.
Using Built-In Transforms, you can apply various transformations to your dataset, such as feature scaling, missing value imputation, and one-hot encoding. Built-In Transforms can also handle large datasets by automatically splitting the data into smaller batches, processing them in parallel, and merging the results into a single output file. This approach enables you to preprocess large datasets in a shorter time, which can be crucial when working with large datasets.
In summary, the most appropriate SageMaker feature to use in this scenario is Built-In Transforms. This feature allows you to perform efficient and scalable feature engineering on your dataset before training your XGBoost algorithm-based model. By using Built-In Transforms, you can extract valuable insights from your dataset and build an accurate prediction model.