A data science team wants a managed service that lets them build, train and deploy their own machine learning models without setting up and patching the underlying training infrastructure themselves. Which AWS service is purpose-built for this end-to-end machine learning work?
- AAmazon Athena, which lets the team run standard SQL queries directly against stored data so they can explore datasets before any analysis begins
- BAmazon SageMaker, which gives the team managed tools to build, train and deploy their own machine learning models on infrastructure AWS operates Correct
- CAmazon Kinesis, which collects and processes streaming data in real time so the team can react to events as the events arrive at the service
- DAmazon QuickSight, which builds interactive dashboards and visual reports so the team can present findings to business users across the company
Why A is wrong: Athena queries data with SQL and is useful for exploring datasets, but it does not build, train or deploy machine learning models.
Why B is correct: SageMaker is the managed machine learning service that covers building, training and deploying models, so the team avoids running training infrastructure themselves.
Why C is wrong: Kinesis ingests and processes streaming data, which is tempting for live data, but it is not the service used to build and train models.
Why D is wrong: QuickSight visualises data in dashboards for reporting, so it presents results rather than building or training machine learning models.