An introduction to Snorkel and data-centric AI
In this ebook, you’ll learn
- How manual labeling blocks enterprises from scaling AI
- The benefits of adopting data-centric AI
- What is Snorkel?
- How Snorkel works under the hood
- How Snorkel Flow has been used by Fortune 500 AI/ML teams to build production models
- Why Snorkel flow has been proven to accelerate AI development by 10-100x
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Learn how to unblock AI with Snorkel and programmatic data labeling
Snorkel’s journey began at the Stanford AI lab in 2015, where the Snorkel AI founding team started studying the then largely overlooked problem of labeling and managing the training data machine learning models learn from.
The not-so-hidden secret of AI is, even today, this training data requires vast volumes of painstaking manual labeling effort. Convinced that there had to be a better way than hand-labeling, we’ve spent over eight years creating new programmatic approaches to labeling, augmenting, structuring, and managing training data and championed a data-centric approach to AI development.
With Snorkel, you create massive amounts of labeled training data programmatically in a matter of hours instead of weeks or months of labeling data manually. Snorkel unlocks a fundamentally new, faster, and more practical way to develop AI.
You’ll see how Snorkel has been used in
- Financial services, to accelerate the development of an AI-driven KYC collusion, and to quickly build AI applications that extract and classify information from critical clauses in loan document
- Healthcare, to extract chronic disease data from clinical trials and to label medical datasets replacing person- years of hand-labelin
- Technology, to replace high-cost, high-latency crowdsourcing of labeled training data