Applied AI

Demo: Using Snorkel Flow to train Microsoft Azure Form Recognizer models

January 5, 2023
2 min read

Last month Snorkel AI highlighted how we’re deepening our partnership with Microsoft Azure AI to help enterprises and government agencies solve their most impactful problems. One example of how Snorkel AI helps organizations leverage Azure AI services for proprietary data and custom objectives is the new Snorkel Flow integration for Microsoft Azure Form Recognizer (currently in private preview).  

Azure Form Recognizer is an AI service that provides pre-built and customizable models for analyzing forms and PDFs. In addition to pre-built models supporting standard forms like W-2s, invoices, receipts, business cards, etc., Form Recognizer includes custom training support to fine-tune its powerful neural document models and support proprietary datasets, non-standard formats, and custom objectives. 

Check out this demo to see how Snorkel Flow can be used to easily and quickly train Azure Form Recognizer custom models. In this demo, we showcase the integration with a real-world use case from real estate and construction. Companies in this space manage massive volumes of forms and contracts with high variability depending on the year or specific process being followed. Without Snorkel Flow, it would require significant manual effort to extract specific key dollar amounts (such as the original contract amount, the current contract amount, and any contract value modifications included in the document) from a large data set of real estate and construction contracts. 

The demo also shows how Snorkel Flow can be used to orchestrate document content preprocessing (including OCR, layout information, and more through Form Recognizer), kick off custom Form Recognizer training jobs directly from the UI, and auto-generate performance analyses over custom Form Recognizer models to guide your next steps.

We’re excited to continue deepening our partnership with Microsoft Azure AI to accelerate AI development for enterprises. Schedule a custom demo tailored to your use case and Azure stack with our ML experts today.

Share this article

Recommended articles

View all articles
agentic-in-action
The Standard for Agents You Can Trust: Lessons from the Federal Front Lines
In the first installment of Agentic in Action — a series about real AI deployments, not demos — Snorkel AI’s Kevin Olivieri sat down with three people who have spent their careers where trust isn’t optional: Chris Sniffen, Federal Applied AI Lead at Snorkel AI; John Hickey, President of August Schell; and Mike Baca, CIO of August Schell. The conversation focused on
June 5, 2026
Snorkel Team
collab-gym-thumbnail
Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration
At our latest Snorkel AI Reading Group, Yijia Shao (Stanford NLP) stopped by our San Francisco office to present Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration. As LLM agents get better at automating tasks on their own, a large class of real-world problems still needs a human in the loop – for their preferences, their domain expertise, or simply for control.
June 4, 2026
Alexis Sobel
Image
Benchtalks #2: The future of coding benchmarks
For our second Benchtalks, the series dedicated to the researchers building the measurement toolkits that frontier labs hill-climb on, Snorkel AI co-founder Vincent Sunn Chen sat down with John Yang, a Stanford PhD student and creator of the SWE-bench franchise, SWE-smith, CodeClash, and most recently ProgramBench. Highlights More on ProgramBench: See the benchmark and the upcoming leaderboard at programbench.com. More from John Yang: Publications and writing at john-b-yang.github.io. Snorkel
June 3, 2026
Vincent Sunn Chen
Image

Join our newsletter

For expert advice, the latest research, and exclusive events.
By submitting this form, I acknowledge I will receive email updates from Snorkel AI, and I agree to the Terms of Use and acknowledge that my information will be used in accordance with the Privacy Policy.