Amazon Rekognition is designed to easily incorporate image and video analysis into applications, all backed by the robust deep learning technology engine developed by Amazon. The technology does not require machine learning expertise for usage and continuously boosts its system with fresh computer vision features.
Amazon Rekognition presents a user-friendly API which can rapidly assess any image or video file that’s held in Amazon Simple Storage Service (Amazon S3). Many sectors, such as advertising, marketing technology, gaming, media, retail and e-commerce bank on images uploaded by their clients to drive user engagement on their platforms. To screen out any inappropriate or offensive content, Amazon Rekognition’s content moderation feature is utilized, thus ensuring the safety of user communities and protection of the brand’s reputation.
Drawing attention to Content Moderation, the 7.0 version introduces 26 new moderation labels and broadens moderation label taxonomy from a two-tier to a three-tier label category. This allows clients to detect finer details in the content that needs to be moderated. The upgraded version also has the ability to distinguish two latest content types, namely animated and illustrated content. Because of these, the customers can fashion more nuanced rules for allowing or vetoing the types of content in their moderation workflow.
Also, Amazon Rekognition Content Moderation provides both batch image moderation and real-time moderation. Clients can access the bulk analysis feature, both on the Amazon Rekognition console or by calling the APIs directly using the AWS CLI (Command Line Interface) and the AWS SDKs (Software Development Kits).
To streamline the moderation process further, Amazon provides a step-by-step guide for using Amazon Rekognition Bulk Analysis. The guide takes you through the setup, execution, and assessment of the bulk analysis feature. From uploading images for moderation to configuring the API for your specific needs, it offers a direct tutorial on using this powerful tool.
Finally, Amazon Rekognition offers users the ability to improve Content Moderation model forecasts with the help of their Bulk Analysis and Custom Moderation features. Users can train a Custom Moderation adapter to enhance the precision of content moderation predictions by verifying the accuracy of the results from their bulk analysis job.
To conclude, Amazon Rekognition continues to propel forward the development and application of image and video analysis technology, making machine learning more accessible and tailored to users, thereby benefitting industries that depend heavily on user-generated content.
