The demand for media content in the form of audio, video and images is growing at an unprecedented rate. However, the process of producing, distributing, and monetising this content is often complex, expensive and time consuming. Applying AI and machine learning (ML) capabilities like image and video analysis, audio transcription, machine translation, and text analytics can solve many of these problems. AWS says it is continuing to invest with technology and consulting partners to meet customer requests for ready-to-use ML capabilities across the most popular use cases.

The new platform is designed to allow users to analyse their media, improve content engagement rates, reduce operational costs, and increase the lifetime value of media content. They can choose turnkey solutions from participating AWS Partners or use AWS Solutions to enable rapid prototyping. These solutions cover four use cases: content search and discovery, captioning and localisation, compliance and brand safety, and content monetisation.

AWS AI services that work with AWS MI include Amazon Rekognition for image and video analysis, Amazon Transcribe for audio transcription, Amazon Comprehend for natural language comprehension and Amazon Translate for language translation. These enable AWS MI to process and analyse media assets to automatically generate metadata from images, video and audio content for downstream workloads at scale.

“The volume of media content that our customers are creating and managing is growing exponentially. Customers can dramatically reduce the time and cost requirements to produce, distribute and monetise media content at scale with AWS Media Intelligence and its underlying AI services," Swami Sivasubramanian, VP of Amazon Machine Learning at AWS.

"We are excited to announce these solutions backed by a strong group of AWS Technology and Consulting Partners. They are committed to delivering turnkey solutions that enable our customers to easily achieve the benefits of ML transformation while removing the heavy lifting needed to integrate ML capabilities into their existing media content workflows.”