![]() Press – Read the latest news about Snorkel AI and Snorkel Flow customers.Frequently asked questions – Explore frequently asked questions on scalable AI development, Snorkel AI, and Snorkel Flow.Events – Attend events to discover new ideas and make connections with AI/ML practitioners.Blog – Browse the latest posts from Snorkel AI experts, customers, and industry leaders.Snorkel research project – Read how Snorkel open source has advanced to production as the Snorkel Flow platform.Programmatic labeling – See how programmatic labeling breaks through the primary bottleneck facing AI.Weak supervision – Explore weak supervision approaches and how they accelerate training data creation.Data-centric AI – Learn about the impact of moving from model-centric to data-centric AI.Sentiment analysis – Tackle complex NLP challenges with nuanced sentiment analysis apps.Conversational AI – Automate responses to customer inquiries to improve efficiency and customer satisfaction.Information extraction – Collect useful text and data from virtually any table or form with flexible extraction apps.Named entity recognition – Solve domain-specific syntactic and semantic challenges with precise NER apps.Document classification – Improve performance by exploiting features unique to your data with custom classification apps. ![]() AI for telecom – Assess network health, tailor customer support, and detect security risks.AI for insurance – Detect fraud, speed claims processing, and improve underwriting workflows.AI for government – Build machine learning models and AI applications across a wide variety of missions and use cases.AI for healthcare – Speed clinical trial success, improve patient outcomes, and enhance research.AI for banking – Personalize customer interactions, manage risk, and improve resource utilization.Enterprise – Benefit from enterprise-grade interoperability, security, expertise, and more.Foundation models – Bridge adaptation and deployment gaps to use foundation models for enterprise production use cases.Model training and analysis – Continuously update and analyze models to guide rapid iteration and improvement.Data labeling – Label programmatically by distilling expertise into functions that power intelligent auto-labeling. ![]()
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