An ML engineer wants to deploy a workflow that processes streaming IoT sensor data and periodically retrains ML models. The most recent model versions must be deployed to production.
Which service will meet these requirements?
A.
Amazon SageMaker Pipelines
B.
Amazon Managed Workflows for Apache Airflow (MWAA)
Amazon SageMaker Pipelines is purpose-built for orchestrating end-to-end ML workflows, including data ingestion, training, evaluation, and deployment. It supports automation, versioning, and deployment of the latest model versions.
MWAA orchestrates general workflows but lacks ML-native features. Lambda cannot handle long-running ML training. Spark processes data but does not manage ML lifecycle.
Therefore, Option A is the correct AWS-native solution.
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