Data-centric AI starts here
Opening Keynote
Bridging the gap between LLMs and enterprise AI
At the crux of AI deployment is managing enterprise data. Getting value out of AI hinges on enterprise specific data. Join me and other AI leaders as we discuss how to build and deploy specialized LLMs in the enterprise.
Featured Speakers
James Lin
Head of AI/ML Innovation
Femi Agboola
Managing Director, Citi Productivity
Vinny DeGenova
Associate Director of Machine Learning
Conference Party
Day 1
Oct 16Hands-On Training Day
Attendees will learn the fundamentals of AI data development and how to apply them with Snorkel Flow – for both predictive and GenAI use cases.
The workshops will include exercises which walk attendees through the process of curating training data and using it to fine-tune and evaluate an LLM for specialized tasks in the enterprise.
- The morning workshop will include an exercise on intent classification for chatbots.
- The afternoon workshop will include an exercise on information extraction from PDF documents. SOLD OUT!
What to expect
- Learn about the principles of AI data development
- Get hands-on with the Snorkel Flow platform
- Curate training data and fine-tune an LLM
- Build a model to classify customer requests
- Build a model to extract information from financial documents
Ink 48 Hotel, 653 11th Avenue,
New York, NY 10036
Day 2
Oct 17Conference Day
Lavan 641 Midtown,
New York, NY 10036
Registration and Breakfast
Opening Keynote: Bridging the Gap Between LLMs and Enterprise AI
Alex Ratner
At the crux of AI deployment is managing enterprise data. Getting value out of AI hinges on enterprise specific data. Join me and other AI leaders as we discuss how to build and deploy specialized LLMs in the enterprise.
Research Spotlight
Fred Sala
Coffee Break
Graduating from Data Labeling to AI Data Development
Chris Glaze
Angela Fox
At the core of Snorkel’s approach and platform is the concept of programmatic data development, which we have repeatedly proven to accelerate training of predictive models by leveraging SME knowledge to produce labels efficiently. At Snorkel Research we have been extending this approach to Generative AI, where the output is not just labels but long form and complex. Our strategy has been to develop methods and tools to keep SMEs in the loop of training and evaluation with scalable processes.
In this presentation, Chris Glaze, Principal Research Scientist at Snorkel AI will give a brief history of how Snorkel evolved from data labeling to data development for Generative AI, and share stories on how Snorkel’s applied this approach to Fortune 500 use cases. Angela Fox, Staff Product Designer at Snorkel AI, will demonstrate how users can leverage Snorkel Flow’s unique programmatic labeling features to solve both predictive and generative business challenges.
Experian: The Importance of LLM Evaluation for Domain-Specific Use Cases
James Lin
James Lin will discuss the importance of LLM evaluation along with common challenges and strategies to overcome them.
Unlocking Hidden Insights: Snorkel’s Solution for Complex and High-Value PDF Documents
Jennifer Lei
PDF documents represent a vast, untapped source of enterprise data, posing unique challenges due to their diverse formats and structures. From messy, massive files to varying types of content such as text, multi-columns, and tables, extracting valuable insights from these documents requires innovative solutions.
In this session, we will explore how to unlock the full value of your PDF documents, from simple classification to the most complex, high-value extraction use cases, all using a single Snorkel Flow platform. By leveraging OCR and parsing, using a collaborative platform for domain experts and data scientists, and utilizing leading LLMs or models of your choice, you can easily overcome format variations, ensure accurate information extraction, and efficiently build custom PDF use cases.
Join us to discover how Snorkel AI’s integrated platform can help you navigate the complexities of PDF document processing and fully leverage the potential of your enterprise data.
How Citi is Succeeding with AI in Banking
Aarti Bagul
Femi Agboola
Aarti Bagul will sit down with Femi Agboola to discuss how Citi is approaching the adoption of AI within banking, early challenges and obstacles, lessons learned and how to build an AI strategy which leads to proper expectations, successful deployments and the delivery of real value.
Lunch
Evaluating LLM Systems
Rebekah Westerlind
Venkatesh Rao
LLM evaluation is critical for generative AI in the enterprise, but measuring how well an LLM answers questions or performs tasks is difficult. Thus, LLM evaluations must go beyond standard measures of “correctness” to include a more nuanced and granular view of quality.
In practice, enterprise LLM evaluations (e.g., OSS benchmarks) often come up short because they’re slow, expensive, subjective, and incomplete. They leave AI initiatives blocked because there is no clear path to production quality.
In this session, Vincent Sunn Chen, Founding Engineer at Snorkel AI, and Rebekah Westerlind, Software Engineer at Snorkel AI, will discuss the importance of LLM evaluation, highlight common challenges and approaches, and explain the core concepts behind Snorkel AI's approach to data-centric LLM evaluation.
Join us to learn more about:
- Understanding the nuances of LLM evaluation
- Evaluating LLM response performance at scale
- Identifying where additional LLM fine-tuning is needed
How Wayfair is Transforming Customer Experiences with Data-Centric AI
Vinny DeGenova
Learn how Wayfair is harnessing the power of machine learning and data to make it easier for customers to find the exact home products they’re looking for with Snorkel AI.
You’ll find out how highly accurate product tags can be extracted from supplier-provided labels and product images to clean and enrich online catalogs. This delivers higher-quality content for customers and the ability to quickly adapt as customer searches evolve. Bottom line - they were able to increase their add-to-cart rates, reduce their cart abandonment rates, and increase their average order size and customer lifetime value.
Enhancing RAG Pipelines for Enterprise-Specific Tasks: Strategies for Accuracy and Reliability
Bryan Wood
In the realm of LLM-powered AI applications, Retrieval-Augmented Generation (RAG) is a pivotal component for enterprise use cases. However, to ensure responses are consistently accurate, helpful, and compliant, RAG pipelines must undergo meticulous optimization.
Critical to this process is the incorporation of only the most relevant information as context. This can be achieved through techniques such as semantic document chunking, fine-tuned embeddings, reranking models, and efficient context-window utilization.
In this presentation, we will:
- Introduce fundamental RAG concepts and outline a standard pipeline.
- Detail optimization strategies for each stage of a sophisticated RAG pipeline to ensure the LLM receives proper context.
- Demonstrate how to leverage Snorkel Flow to optimize RAG pipelines.
By attending, you will gain insights on how to:
- Enhance LLM responses by minimizing retrieval errors.
- Fine-tune various stages of the RAG pipeline.
- Expedite the deployment of production-grade RAG applications.
Join us to elevate your RAG systems and drive superior AI outcomes for your enterprise.
AI From the Trenches: Lessons Learned from Practitioners on the Front Lines
Alex Shang
André Balleyguier
Elena Boiarskaia
Gabe Smith
A moderated panel discussion featuring Snorkel machine learning engineers who’ve collaborated with some of the largest enterprises in the world to successfully build and deploy production AI/ML models.
The discussion will focus on the most common challenges faced by AI/ML engineers and data scientists, from expectation setting and use case prioritization to technical decisions and challenges. You’ll hear first hand about the lessons learned and best practices developed by Snorkel ML engineers, as well as recommendations for getting started, moving past PoCs and successfully delivering on the promise of AI into the enterprise.
Fine-Tuning and Aligning LLMs with Enterprise Data
Marty Moesta
Amit Kushwaha
LLMs often require fine-tuning and alignment on domain-specific knowledge before they can accurately, and reliably, perform specialized tasks within the enterprise.
The key to transforming foundation models such as Meta's Llama 3 into specialized LLMs is high-quality training data which can be applied via fine-tuning and alignment.
In this session, we'll provide an overview of methods such as SFT and DPO, show how to curate high-quality instruction and preference data 10-100x faster (and at scale) and demonstrate how to fine-tune, align and evaluate an LLM.
Join us, and learn more about:
- Curating high-quality training data 10-100x faster
- Emerging LLM fine-tuning and alignment methods
- Evaluating LLM accuracy for production deployment
Delivering Business Value with Data-Centric AI in Financial Services
Peter Williams
Bryan Wood
Closing Keynote: Future of Snorkel
Ronaldo Ama
Ajay Singh
Fireside Chat: AI Success in the Enterprise
Alex Ratner
Murli Buluswar
Mix and Mingle
Conference Party with Magician, Alexander Boyce
Alexander Boyce
The drinks will flow. The food will delight. The magic tricks will astound. Party with sleight-of-hand virtuoso Alexander Boyce, who’ll ensure your night is full of laughter, wonder, and inspiration.
Still need a place to stay?
WE’RE
SOLD OUT!