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AI Agents & Multimodal LLMs

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Mark Kim-Huang

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What you'll learn

Why and When to Use Task Adapted Models

Determine when you should consider using adapted models for table reasoning, pdf parsing or sentiment analysis.

Fine-tune LLMs for Task Adaptation

Use low code APIs to train a base model for a specific task.

How to Shift Towards Agentic Workflows

Learn about how task-adapted LLMs can help move your business closer towards deploying autonomous assistants.

Why this topic matters

LLMs are not a one size fits all and generic models may not be able handle the complexity and intricacy of the tasks that involve tabular reasoning or document parsing. Leveraging a task-specific model can help save costs for operational workflows such as financial compliance and fraud detection, all while moving your business closer towards an agentic future.

You'll learn from

Mark Kim-Huang

Co-Founder & Chief Architect at Gradient

Mark is a co-founder and Chief Architect at Gradient, a full stack AI platform that enables businesses to build customized agents to power enterprise workloads. Known for his pioneering work in LLMs and fine-tuning, Mark is a frequent contributor to the AI and MLOps community. Prior to Gradient, Mark led machine learning teams at Splunk and Box, transitioning over from a nearly decade-long career as an algorithmic trader at quantitative hedge funds like Stevens Capital, Paloma Partners, and TD Securities. 

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