Schlumberger is the world’s leading provider of technology and services for the energy industry, operating in over 120 countries. The company provides well maintenance and analytics services to the world’s biggest oil companies, and it believes that large-scale data analysis and artificial intelligence/machine learning will help them remain a leader in the market. One way they’ve been able to achieve this is by building their own AI application using Snorkel Flow to automatically extract geological entities and critical field data across a variety of document structures and report types they receive from their customers.
This blog post introduces variants of Precision, Recall, and F1 metrics called Precision Gain, Recall Gain, and F1 Gain. The gain variants have desirable properties such as meaningful linear interpolation of PR curves and a universal baseline across tasks. This post explains what these benefits mean for you, how the gain metrics are calculated and outline some examples for intuitive comparison.
On the heels of the second annual Future of Data-Centric AI event, we’re energized by what we learned from data scientists, machine learning engineers, and AI leaders who are adopting data-centric approaches to accelerate AI success. The Snorkel Flow platform provides these teams with a seamless workflow across training data creation, model training, and analysis—the scaffolding to make data-centric AI…
Continuous Model Feedback, available in beta as part of the new Studio experience, is Snorkel Flow’s latest capabilities to make training data creation and model development more integrated, automated, and guided.
Snorkel AI just hosted the second day of The Future of Data-Centric AI conference 2022. Across 40+ sessions, 50+ Data scientists, ML engineers, and AI leaders came together to share insights, best practices, and research on adopting data-centric approaches with thousands of attendees from all around the world. Aarti Bagul, a Snorkel AI ML Solutions Engineer and one of the…
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 two days with 40+ sessions, 50+ speakers, and thousands of…
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 is used across the financial industry. By processing and extracting…
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 to learn is more often the biggest determiner of AI…
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, and other distinct business problems. However, they also require an…
