All raw data for an ML project is stored in Amazon S3. An ML engineer must build data ingestion pipelines and model deployment pipelines on AWS. Which combination of services should the engineer use?

The correct answer is to use AWS Glue for data ingestion pipelines and Amazon SageMaker Studio Classic for model deployment pipelines. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. It is well-suited for building ETL (Extract, Transform, Load) pipelines to process raw data from S3. Amazon SageMaker Studio Classic provides a unified web-based IDE for all ML development steps, including building, training, and deploying models, making it ideal for creating and managing model deployment pipelines. Incorrect options: Amazon Kinesis Data Firehose is primarily for streaming data ingestion, not batch processing of raw data already in S3. Amazon Redshift ML is for building, training, and deploying ML models directly within Amazon Redshift, not for general data ingestion from S3. Amazon Athena is an interactive query service for S3 data and is not designed for building robust data ingestion pipelines with transformation capabilities. While SageMaker notebooks can be used for deployment, Studio Classic offers a more comprehensive and integrated environment for pipeline management.