Georgetown University's CSET Accelerates AI Development with Snorkel Flow
公司规模
1,000+
地区
- America
国家
- United States
产品
- Snorkel Flow
技术栈
- NLP
- Active Learning
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 自然语言处理 (NLP)
适用行业
- 教育
- Software
适用功能
- 产品研发
- 质量保证
用例
- 自动化疾病诊断
- 质量预测分析
服务
- 数据科学服务
- 软件设计与工程服务
关于客户
Georgetown University's Center for Security and Emerging Technology (CSET) is a leading research organization focused on studying the security implications of emerging technologies. The center aims to provide policymakers with data-driven analysis and recommendations to navigate the complex landscape of technological advancements. CSET's data science team is dedicated to developing advanced AI models to classify and analyze scientific articles, particularly in the field of virology. The team faced significant challenges in managing the manual labeling workflows required for their NLP applications, which led them to seek more efficient and scalable solutions.
挑战
CSET's data science team wanted to build NLP applications that classify scientific articles such as technical papers on virology. The team realized that manual labeling workflows would be impractical for the task.
解决方案
With the help of integrated analysis tools within Snorkel Flow, the team was able to pinpoint data slices for domain expert spot-checks and troubleshooting to improve accuracy. This approach powered an active learning workflow, significantly enhancing the efficiency of their labeling process. Snorkel Flow's advanced features, such as autosuggest and cluster labeling functions (LFs), enabled the team to create 107,000 programmatic labels. This not only reduced the labeling time by 50% but also improved productivity and accuracy. Within days, the team achieved 85% accuracy on a classification model, demonstrating the effectiveness of the solution.
运营影响
数量效益
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