AI/ML and Analytics Services
Get to know the main AWS AI/ML and analytics services: the three-layer AI stack, pre-trained services like Rekognition and Comprehend, Amazon SageMaker, generative AI with Bedrock and Q, and analytics tools like Athena, Redshift, and Kinesis.
AI/ML and Analytics Services covers the AWS services that turn data into insight and add intelligence to applications. For the CLF-C02 exam, the goal is not to train a model or write a query. It is to recognize each service by name and match a described problem to the one that fits.
The topic has two lessons. The first walks through the AWS AI and ML stack, from ready-made services you call through an API up to the Amazon SageMaker platform and generative AI. The second follows data through an analytics pipeline and compares the services learners mix up most.
What This Topic Covers
- the three layers of the AWS AI and ML stack, from pre-trained services to custom models
- pre-trained AI services such as Rekognition, Comprehend, Polly, Transcribe, and Lex, and the task each one handles
- Amazon SageMaker as the platform for building, training, and deploying your own models
- generative AI with Amazon Bedrock and Amazon Q
- the stages of an analytics pipeline: ingest, store, process, query, and visualize
- core analytics services including Amazon Athena, Redshift, EMR, AWS Glue, Kinesis, and QuickSight
- how to tell Amazon Athena, Redshift, and EMR apart
Why It Matters
These services show up often as scenario questions, where a sentence describes a need and you pick the service. Knowing that Transcribe turns speech into text, that SageMaker is for custom models, or that Athena runs SQL on S3 lets you answer quickly without second-guessing.
The topic also rewards pattern recognition over memorizing detail. Once you can place a service in the right layer of the AI stack or the right stage of the analytics pipeline, the long list of names becomes a small set of clear jobs, which is exactly what the exam tests.