Meta AI Researchers Built An End-To-End Machine Learning Platform Called Looper, With Easy-To-Use APIs For Decision-Making And Feedback Collection | Mark Tech Post

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Looper is a comprehensive artificial intelligence platform for optimization, personalization, and growth. 

Looper is a fully integrated artificial intelligence platform for optimization, customization, and feedback collecting developed by Meta Researchers. Looper can support the whole machine learning lifecycle, from model training through deployment and inference and product assessment and optimization. The Looper platform now hosts 700 AI models and generates 4 million AI outputs every second. It is intended for use cases with limited data volumes and model complexity that demand simplicity and speedy implementation. Looper runs in real-time, in contrast to many other AI systems, which make inferences in batch mode. 

Unlike large-scale AI models for vision, speech, and natural language processing, Looper uses models that can be rapidly retrained and deployed in huge numbers on shared infrastructure. A/B testing may investigate several models and decision rules, such as those employed by contextual bandits to mimic prediction uncertainty across one or more targets. A simple-to-use AI platform is often the deciding factor in adoption for teams with no previous expertise with production AI. Meta’s platform takes care of software updates, log management, and monitoring, resulting in considerable productivity improvements: It enables product makers to deploy more AI use cases than solutions with a restricted emphasis. Product teams have a spectrum of AI capabilities between beginners and expert AI engineers.



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