Managed AI Services Selection
Understand how to choose appropriate AWS managed AI services such as Rekognition, Transcribe, Translate, Comprehend, Textract, Polly, and Bedrock based on specific use cases. Learn decision criteria to distinguish when to use managed APIs or train custom models with SageMaker, enabling efficient deployment and maintenance of scalable ML solutions on AWS.
Selecting the right AWS managed AI service for a given use case is one of the most frequently tested skills on the AWS Certified Machine Learning EngineerAssociate exam. AWS provides a spectrum of AI and ML capabilities, from fully managed, pretrained APIs that require no ML expertise to custom model-training pipelines built on SageMaker. For common tasks such as image analysis, speech-to-text conversion, language translation, and natural language processing, AWS offers ready-to-use services that accept API calls and return predictions without model training, data labeling, or infrastructure provisioning.
The managed AI services covered in this lesson are Amazon Rekognition for image and video analysis, Amazon Transcribe for speech-to-text, Amazon Translate for language translation, Amazon Comprehend for NLP and text analytics, Amazon Textract for document data extraction, and Amazon Polly for text-to-speech conversion. Alongside these, Amazon Bedrock is the managed service for accessing foundation models and generative AI workloads. The central question this lesson addresses is straightforward but critical for the exam: How do you decide which service fits a given scenario, and when should you avoid custom ...