MLA-C01 Question 205
Select 2A company is building a machine learning pipeline to predict customer churn using Amazon SageMaker. They need to streamline the process of feature engineering, model training, and hyperparameter optimization. Additionally, they want to ensure that the pipeline can be triggered automatically when new customer data is uploaded to an S3 bucket. Which combination of AWS services is best suited for this task?
- A
Amazon SageMaker Processing
- B
Amazon SageMaker Pipelines
- C
AWS Lambda
- D
Amazon SageMaker Autopilot
- E
AWS Glue
Show answer and explanation
Correct answers: B, C
Explanation
Amazon SageMaker Pipelines provides a managed solution for building, automating, and managing end-to-end machine learning workflows. Combined with AWS Lambda, which can trigger these workflows based on S3 events, these services address the requirement for both automation and event-driven execution.
- A. Incorrect.
Amazon SageMaker Processing is useful for preprocessing and feature engineering, but it does not provide orchestration of the entire pipeline or event-driven triggers.
- B. Correct.
Amazon SageMaker Pipelines is specifically designed to orchestrate and automate machine learning workflows, making it essential for this task.
- C. Correct.
AWS Lambda is ideal for event-driven processing and can trigger the pipeline when new data is uploaded to the S3 bucket.
- D. Incorrect.
Amazon SageMaker Autopilot simplifies model training and tuning but does not provide end-to-end pipeline orchestration or event-driven triggers.
- E. Incorrect.
AWS Glue is a data integration service that can be used for ETL tasks but is not directly relevant to orchestrating and automating machine learning pipelines.