The Future of Data-Centric AI 2022 day 1 highlights 
- Snorkel AI just hosted the first day of The Future of Data-Centric AI conference 2022. This conference brings together data scientists, ML engineers, and AI leaders to share insights, best practices, and research on how to evolve the ML lifecycle from model-centric to data-centric approaches. This conference takes place over… ... 
10-Ks information extraction case studies 
- Building NLP techniques to understand 10-Ks is time-consuming, costly, and challenging. In this post, Machine Learning Engineer, Aarti Bagul discusses three information extraction case studies on how banks around the world are building highly accurate NLP applications using Snorkel Flow's AI platform. From retail banking to hedge fund investing, NLP… ... 
Introducing Cluster View: Instant data insight made actionable to speed AI development 
- Programmatic labeling moves a classic technique from interesting to high-impact So much of real-world AI development entails working with text data that’s messy — in fact, 80%+ of enterprise data is unstructured. And while state-of-the-art models get a lot of the glory, creating the training data that conveys what your model needs… ... 
Data-centric approaches to multi-label classification 
- AI systems are well-suited to tasks involving recognizing and predicting data patterns. Supervised classification systems categorize unseen data into a finite set of discrete classes by learning from millions of hand-labeled labeled sample points. These classifiers are powerful business tools – they automate document sorting, customer sentiment analysis, sales performance,… ... 
Data annotation guidelines and best practices 
- What is data annotation? Data annotation refers to the process of categorizing and labeling data for training datasets. In order for a training dataset to be usable, it must be categorized appropriately and annotated for a specific use case. With Snorkel Flow, organizations can annotate high-quality labeled training data via… ... 
3 ways to use Snorkel’s Labeling Functions 
- Labeling functions are fundamental building blocks of programmatic labeling that encode diverse sources of weak labeling signals to produce high-quality labeled data at scale. Let’s start with the core motivation for labeling functions: over time, every major commercial organization and government agency builds various valuable, often bespoke knowledge resources. These… ... 
Clinical entity classification in electronic health records 
- Research recap: Ontology-driven weak supervision for clinical entity classification in electronic health records (EHRs) In this post, I have summarized the research published in this academic paper, Ontology-driven weak supervision for clinical entity classification in electronic health records by Jason Fries et al. This paper was published in Nature Communications… ... 
Building AI models for financial document processing best practices 
- Highlighting the best practices for building and deploying AI models for financial document processing applications AI has massive potential in the financial industry. Building AI models to automate information extraction, fraud detection, and compliance monitoring can provide efficient and faster responses and support repurposing domain experts' labor to more meaningful… ...
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