Azure AI Content Safety

Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases. With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required.

Hive’s complete solution to protect your platform. Mobilizing the world's largest distributed workforce of humans labeling data, we are raising the bar for automated content moderation. We offer both best-in-class models as well as manual moderation, allowing us to provide solutions at scale and outperform contract workforces of business process outsourcers (BPOs). In addition to our best-in-class models, our distributed workforce can meet a variety of manual moderation needs. Whether you want to manually moderate user content or annotate training data at scale, our distributed system and consensus policy provide a level of precision that our competitors cannot.