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Snorkel Enterprise AI Platform
Predictive ML
Train and deploy predictive models for classification and information extraction tasks faster, and with higher accuracy, using programmatic data labeling, efficient SME-in-the-loop collaboration, and visual error analysis—all within a single platform.
Programmatic data labeling for predictive ML
Predictive models, whether for classification or information extraction, require high-quality labeled data for training. However, manual data labeling and annotation is time-consuming, expensive, and blocks enterprise AI/ML projects from reaching production.
The solution is programmatic data labeling with weak supervision. It provides ML engineers and data scientists with an efficient way to apply subject matter expert (SME) domain knowledge to an entire dataset at once, reducing the load on SMEs without sacrificing accuracy.
Curate training data faster, and build more accurate models
Streamline data scientist and SME collaboration
Snorkel Flow provides data scientists and SMEs with a collaborative AI data development platform so they don’t have to waste time filling out and passing around spreadsheets. SMEs can annotate ground truth data in place as well as share feedback and domain knowledge via tags and comments on labeled data in test, training, and validation datasets.
Create a baseline with LLM-generated labels
Snorkel Flow’s Warm Start feature allows data scientists and SMEs to easily, and quickly, label an entire dataset by prompting foundation models such as OpenAI GPT, Google Gemini, and Meta Llama. Further, Snorkel Flow can improve label accuracy by prompting multiple LLMs and choosing the best response.
Scale with templatized labeling functions
Snorkel Flow includes out-of-the-box templates for a broad range of labeling functions, making it easy to encode SME domain knowledge and apply it to entire datasets. There are templates for everything from keyword searches and pattern matching to automatically generated embedding spaces and custom Python functions via built-in notebooks.
Improve accuracy with guided error analysis
Snorkel Flow includes visual error analysis tools which highlight label confidence, conflicts between predicted labels and ground truth, and recommendations for creating or updating label functions to improve label accuracy—providing data scientists with the insight needed to uncover errors in ground truth and iterate on training data.
Dive deeper into predictive ML with these resources
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