MLA-C01 Question 204
Single answerYou are developing a machine learning pipeline on AWS to predict customer churn. Your data is stored in Amazon S3, and the pipeline involves preprocessing, model training, and deployment. You need a service that allows you to easily orchestrate and automate the pipeline steps while integrating seamlessly with other AWS services like SageMaker for training and S3 for data storage. Which AWS service is the most appropriate for this task?
- A
AWS Step Functions
- B
AWS Glue
- C
Amazon SageMaker Pipelines
- D
Amazon EMR
Show answer and explanation
Correct answer: C
Explanation
Amazon SageMaker Pipelines is the most appropriate service for this use case because it is specifically designed for orchestrating machine learning workflows on AWS. It provides built-in features for pipeline automation, model training, and deployment, and integrates seamlessly with other AWS services such as SageMaker and S3. While other services like Step Functions and Glue have their own strengths, they do not offer the specialized ML workflow management capabilities of SageMaker Pipelines.
- A. Incorrect.
AWS Step Functions is a workflow orchestration service, but it is not purpose-built for machine learning workflows. It can be used for ML tasks, but Amazon SageMaker Pipelines is more suitable for this specific use case.
- B. Incorrect.
AWS Glue is primarily used for data integration and ETL (Extract, Transform, Load) purposes. While it can preprocess data, it is not designed to orchestrate end-to-end machine learning pipelines.
- C. Correct.
Amazon SageMaker Pipelines is specifically designed for building, automating, and managing end-to-end machine learning workflows. It integrates seamlessly with SageMaker for training, S3 for data storage, and other AWS services, making it the best choice for this scenario.
- D. Incorrect.
Amazon EMR is a big data processing service used for distributed data processing with frameworks like Hadoop and Spark. It is not the ideal choice for orchestrating an ML pipeline.