Insurance claims processing has long required a lot of tedious and expensive human labor, but artificial intelligence (AI) can help.

Gideon Mann, head of ML Product and Research at Bloomberg LP, chatted with Snorkel CEO Alex Ratner about building BloombergGPT.

Snorkel Flow makes it easy to fine tune LLMs like GPT-3.5 Turbo to work better for specific domain and enterprise requirements.

Sessions at the Future of Data-Centric AI covered LLMs, gen AI, and more. All recordings are now publicly available. See them here!

Most data science leaders expect to customize LLMS, but the process of making LLMs work for your business is still a fresh challenge.

Ai and ML offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management.

Data labeling remains a core requirement for machine learning projects—especially in the age of genAI and LLMs. Here’s a handy guide.

Regulators and compliance officers face a constantly evolving landscape of financial markets. Rule-based systems struggle where AI succeeds.

The medical industry is exploding with data. Manually labeling data for clinical trials is a challenge. Fortunately, AI can help.

Professionals in the data science space often debate whether RAG or fine-tuning yields the better result. The answer is “both.”

Past U.S. Chief Data Scientist DJ Patil talked with Snorkel AI CEO Alex Ratner on topics including the origin of the title “data scientist.”

We designed, implemented, and rolled out a multi-faceted autoscaling solution that expands our ML capabilities while saving on cloud costs.

The surest way to improve foundation models is through more and better data, but Snorkel researchers showed FMs can learn from themselves.

GPT-3 unlocked additional capacity by automating first drafts of internal updates—including blog summaries and sample tweets.

Handling complaints effectively and efficiently with AI is essential to maintain customer satisfaction and protect the bank’s reputation.

The following was originally published on Wayfair’s tech blog. We have cross-posted it here, edited only to fit Snorkel’s formatting guidelines. — One of our missions at Wayfair is to help our 22 million customers find the products they are looking for. For example, when a customer searches for a “modern yellow sofa” on Wayfair, we want to show the most…

Generative AI can write poems, recite common knowledge, and extract information. GenAI can also help quickly build predictive pipelines.

Experts named generative AI as the most transformative technology of the decade. What is genAI, how does it work and why does it matter?

As enterprises look toward deploying LLM-powered, business-critical applications, they’re learning to use strategies beyond prompting.

Recent developments in AI tools have made email surveillance for banks better than ever. See how foundation models and Snorkel Flow can help.

Getting better performance from foundation models (with less data)

GenAI may be the most transformative technology of the past decade but data is where enterprises are able to realize real value from AI today.

Generative AI is at peak hype and poised to dive into the “trough of despair,” according to the 2023 Gartner® Hype Cycle™ for AI.

We used weak supervision to programmatically curate instruction tuning data for open-source LLMs to build a better GenAI.

Snorkel AI announced a strategic partnership with Together AI to enable organizations to build their own proprietary LLMs on their data.

This release eases Snorkel Flow application creation process and tightens the iteration loop. It also upgrades our security certifications.

NVIDIA’s Nyla Worker presented “Leveraging Synthetic Data to Train Perception Models Using NVIDIA Omniverse Replicator” in 2022.

Google experts Abhishek Ratna and Robert Crowe discuss practical paths to data-centricity in applied AI at The Future of Data-Centric AI ’22.

State Farm senior data scientist Jason Goldfarb presented “Reusable Data Cleaning Pipelines in Python” at the Future of Data-Centric AI 2022.

Jack Zhou, product manager at Arize, on “How to Apply Machine Learning Observability to Your ML System” from The Future of Data-Centric AI