Snorkel AI hosted SnorkelCon, its first annual user conference in New York City, bringing together hundreds of enterprise AI experts to explore how to drive faster enterprise AI deployment through programmatic data development.
The day’s agenda included glimpses into Snorkel Flow’s future and real-world examples of Snorkel AI customers using the platform to build AI applications that yield real business value faster than ever. Additionally, event sponsors Amazon Web Services, Google Cloud, Microsoft, Databricks, and SambaNova Systems spoke with eventgoers about how their offerings complement Snorkel Flow.
Automation, efficiency, and scaling AI in the enterprise
Presentations at this year’s SnorkelCon centered on three core themes:
- Automation of data labeling.
- Efficiency in AI model specialization.
- Scaling AI initiatives in large enterprises.
Together, these themes united into an urgency to develop repeatable practices for converting unstructured data into reusable assets for model development and training. Closing keynote speaker Murli Buluswar, Citi’s Head of U.S. Consumer Analytics, summarized a major thread for the day when he told the audience that we’re moving from the information age into the “intelligence age.” This age, he noted, will be defined by moving beyond the democratization of data to the democratization of intelligence.
Earlier presentations spotlighted automation as presenters highlighted the growing need to build AI models while minimizing manual labeling efforts. Snorkel Flow’s programmatic AI data development techniques, several presenters noted, address these challenges and can accelerate specialized model development times by up to 100x.
See select session recordings here.
SnorkelCon 2024: a milestone for AI data development in the enterprise
SnorkelCon 2024 showed how a data-centric approach can make AI more efficient, scalable, and impactful for businesses. AI leaders and practitioners shared how they’ve used Snorkel Flow to streamline data labeling, speed up model development, and bring AI into production faster than ever. Real-world case studies brought these ideas to life, illustrating how companies are already seeing measurable results from using Snorkel’s platform.
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Matt Casey
Data Science Content Lead
Matt Casey leads content production at Snorkel AI. In prior roles, Matt built machine learning models and data pipelines as a data scientist. As a journalist, he produced written and audio content for outlets including The Boston Globe and NPR affiliates.
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