SNORKEL AI RAISES $350M AT $3.5B VALUATION

The platform uses human and AI agent collaboration to create and validate training data for machine learning models. Snorkel AI's technology addresses a critical bottleneck in AI development: the need for high-quality labeled datasets. The funding round reflects growing investor interest in infrastructure supporting AI model development. Data quality and preparation remain significant challenges as organizations scale generative AI and machine learning deployments. Snorkel AI's "agentic" approach combines automated AI systems with human oversight, allowing organizations to programmatically generate and refine training data at scale. The company's platform targets enterprises building or fine-tuning machine learning systems. The startup, founded by Stanford researchers, competes in a crowded space of data and ML infrastructure startups seeking to simplify how organizations prepare data for AI applications.