Small models don’t need more parameters, they need better data. I’ll share how my team built the xLAM family of small action models that punch far above their weight, enabling fast and accurate AI agents deployable anywhere. We’ll explore why high-quality, task-specific data is the ultimate performance driver and how it turns small models into powerful, real-world solutions. You’ll leave with a practical playbook for creating small models that are fast, efficient, and ready to deploy from the edge to the enterprise.
talk-data.com
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Speaker
Shelby Heinecke, PhD
2
talks
Senior AI Research Manager
Salesforce
Shelby Heinecke, PhD, leads an AI research team at Salesforce, focusing on autonomous agents, LLMs, and on-device AI for product and research. Her team’s notable projects include the open-source multi-agent platform AgentLite and the Tiny Giant 1B model for function-calling. She earned a PhD in Mathematics from the University of Illinois at Chicago, specializing in machine learning theory, along with an MS in Mathematics from Northwestern and a BS in Mathematics from MIT.
Bio from: Small Data SF 2025
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Shelby Heinecke, PhD
(Salesforce)
,
Benn Stancil
(Mode)
,
Joe Reis
(DeepLearning.AI)
,
George Fraser
(Fivetran)