A media company has a very large archive of unlabeled images, text, audio, and video and needs to quickly index assets so researchers can find relevant content. The team has limited ML expertise. Which approach will produce searchable tags fastest?

The correct approach leverages AWS AI services for immediate results with minimal ML expertise. Amazon Rekognition analyzes images and videos for objects, scenes, and activities. Amazon Comprehend extracts insights from text, such as entities, key phrases, and sentiment. Amazon Transcribe converts audio and video speech into text, which can then be processed by Comprehend. These services are fully managed and provide pre-trained models, making them ideal for quickly indexing diverse, unlabeled media without requiring deep ML knowledge or model training. Creating Amazon Mechanical Turk tasks would be slow and expensive for a "very large archive" and doesn't provide "fastest" indexing. Using Amazon Transcribe then training SageMaker models is more complex and time-consuming than using pre-trained services, requiring ML expertise. Building custom models on AWS Deep Learning AMI on EC2 GPU instances is the most complex and time-consuming option, demanding significant ML expertise and development effort, directly contradicting the requirement for a fast solution with limited ML expertise.