Over the last two years, we’ve been obsessive in our commitment to help our customers get Generative AI (GenAI) into production. We’ve delivered some exciting results, and along the way have built powerful workflows into our product.
Just like traditional machine learning (ML) development, data drives enterprise generative AI development. We’re excited to share the latest product updates to our data-centric workflows for generative AI.
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Our customers are moving beyond “vibe checks” and using Snorkel’s new Generative AI Evaluation Suite to ensure their pipelines are ready for production.
We built our Evaluation Suite on three core principles:
Snorkel allows users to rapidly onboard and evaluate their data. The platform lets users measure adherence to criteria using out-of-the-box or customized criteria and a mix of ground truth-based and evergreen auto-evaluators. Users can also compare the results of multiple experiments side by side.
In Snorkel, users can now programmatically slice their data via slicing functions. Data slices allow users to identify high-priority subsets of inputs like:
AI developers need a platform that transitions them from evaluation to development in a single click. Evaluation dashboards give you direct insight into data errors, and allow users to launch workflows in Snorkel to directly address those errors.
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To complement the new Evaluation Suite, we’ve developed a cookbook, available in our documentation. This provides a guided evaluation experience for your AI teams. If you’re interested in learning how Snorkel’s Evaluation Suite could help your team, please reach out at [insert best way to contact us].
After working closely with our public preview customers, we are thrilled to announce that key improvements to our LLM fine-tuning workflow! As a reminder, our fine-tuning workflow follows five distinct steps:
This release offers core stability improvements, as well as new tooling to enhance your data development efforts when crafting your fine-tuning training sets.
Snorkel offers guided workflows for fine-tuning and alignment via our LLM-fine tuning and alignment cookbook, available in our documentation.
Our annotation work enables data scientists to seamlessly collaborate with subject matter experts (SMEs) to scale their expertise. To help SMEs share their feedback, we introduced two new views for our annotation studio:
Deploy production AI and ML applications 10-100x faster with Snorkel’s experts, using our proprietary technology.
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