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Fine-tune and distill LLMs for enterprise AI and specialized tasks
Accelerate the curation of high-quality training data and deliver smaller, specialized LLMs which meet production accuracy requirements as well as ethical standards, company policies, and industry regulations.
Curate training data faster and deliver highly accurate LLMs for enterprise AI use cases
Streamline SME & data scientist collaboration
Replace spreadsheet passing with a unified platform which makes it easy for SMEs to share domain knowledge, help establish ground truth, and provide feedback.
Break through data labeling bottlenecks
Label thousands of prompt-response pairs in minutes by encoding SME acceptance criteria in labeling functions and applying them to an entire dataset immediately.
Improve the accuracy of specialized LLMs
Identify both ground truth and training data errors using built-in error analysis tools to iterate quickly, and improve the response accuracy of specialized LLMs.
Why enterprise AI applications need specialized LLMs
While foundational models are easy to get started with and can perform a broad range of common tasks, they lack the domain knowledge needed to perform specialized tasks with enough accuracy for production deployment. However, fine-tuning smaller models with high-quality training data produces smaller, specialized LLMs which are not only more accurate, but less expensive to run.
Build smaller, specialized LLMs to increase accuracy, improve performance, and reduce inference costs
Curate high-quality training data faster
Create a diverse set of prompt-response pairs by combining SME domain knowledge with foundation models and synthetic data generation, quickly generating labels for all of them (e.g., accept/reject or rank), and using the highest-quality pairs for LLM fine-tuning and alignment.
Incorporate SME feedback effortlessly
Collaborate with subject matter experts using a single platform to create ground truth based on domain knowledge and human feedback, and to iterate on test, validation, and training datasets by refining labeling logic to further improve label accuracy.
Fine-tune and deploy specialized LLMs
After curating high-quality training data, use it to fine-tune and deploy specialized LLMs by taking advantage of native integration with Databricks Data Intelligence Platform (i.e., Mosaic AI and Unity Catalog) and AWS SageMaker, Google Vertex AI, and Azure Machine Learning.
LLM fine-tuning for enterprise AI
Keep enterprise data private and secure
Avoid leaking sensitive and/or private company data with foundation model providers by using enterprise data to train and serve smaller, specialized models, ensuring your data remains your differentiator.
Apply the latest LLM fine-tuning techniques
Incorporate learnings shared by Snorkel AI research scientists and applied ML engineers, from new distillation methods and enterprise alignment techniques to hybrid SME- and LLM-based taxonomy creation.
Evaluate LLM
accuracy at every step
Take advantage of built-in, customizable LLM evaluation capabilities to measure accuracy in domain-specific contexts, and to understand where a model may require additional training data and why.
Fine-tune and align state-of-the-art foundation models
Dive into LLM fine-tuning with these resources
Deploy specialized AI to production today with Snorkel
Transform your data and expertise into high-quality, specialized AI for generative or predictive applications you can trust in production.
Snorkel Flow
A complete platform for rapid and auditable data labeling, RAG optimization, model fine-tuning, and LLM evaluation. Trusted by enterprise data science teams to build specialized production AI.
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Snorkel Custom
Our team of experts will fast-track specialized model development on your data to reduce model development costs, accelerate time to production, and achieve higher model quality.
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