Labeling training data is increasingly the largest bottleneck in deploying
machine learning systems. We present Snorkel, a first-of-its-kind system that
enables users to train state-of-the-art models without hand labeling any
training data. Instead, users write labeling functions that express arbitrary
heuristics, which can have unknown accuracies and correlations. Snorkel
denoises their outputs without access to ground truth by incorporating the
first end-to-end implementation of our recently proposed machine learning
paradigm, data programming. We present a flexible interface layer for writing
labeling functions based on our experience over the past year collaborating
with companies, agencies, and research labs. In a user study, subject matter
experts build models 2.8x faster and increase predictive performance an average
45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in
this new setting and propose an optimizer for automating tradeoff decisions
that gives up to 1.8x speedup per pipeline execution. In two collaborations,
with the U.S. Department of Veterans Affairs and the U.S. Food and Drug
Administration, and on four open-source text and image data sets representative
of other deployments, Snorkel provides 132% average improvements to predictive
performance over prior heuristic approaches and comes within an average 3.60%
of the predictive performance of large hand-curated training sets.

Snorkel 是一个无需手动标记数据的系统，它基于数据编程的机器学习范式实现了自动去噪，用户可以编写任意启发式函数以训练最先进的模型。在与美国退伍军人事务部和美国食品和药品管理局的两次合作以及代表其他部署的四个开源数据集上，Snorkel 相对于先前的启发式方法提高了 132％的平均预测性能，并且与大型手工筛选的训练集的平均预测性能相差不到 3.60％。