Sanathraj Narayan

Sanathraj Narayan

Data Science Manager

Lam Research

Sanath Raj is an experienced AI/ML professional with over a decade of experience in designing and deploying machine learning solutions. With a strong background in data science, he has worked across industries, specializing in the industrialization of AI models for enterprise applications. Sanath has led multiple AI-driven initiatives and has deep expertise in frameworks like LangChain and AWS SageMaker, enabling organizations to build scalable and production-ready AI solutions. As a speaker at industry conferences, he has shared insights on optimizing LLM performance, embedding strategies, and real-world AI deployments. He also mentors professionals, helping them navigate the evolving landscape of AI and machine learning. Passionate about innovation, Sanath is currently working on integrating LLMs into enterprise workflows and writing a book on LangChain. His mission is to bridge the gap between research and real-world AI adoption, helping businesses unlock the full potential of generative AI.

Get ready for a high-stakes AI face-off as three leading multi-agent frameworks - AutoGen, CrewAI, and LangGraph, go head-to-head solving the same real-world AI problem: Building a Multi-Agent Helpdesk AI Assistant.

Watch top Agentic AI practitioners demonstrate how each framework tackles this challenge: from structuring agent teams to orchestrating decisions across multiple steps. This unique session combines live hands-on demos and a panel discussion. You’ll walk away with a clear view of what each framework does best, where they struggle, and how to pick the right one for your next Agentic AI project.

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This session on LangGraph is on building graph-based LLM workflows. We will explore agent architectures with memory and tools, implement reflexion loops for self-improvement, and build intelligent systems that combine retrieval and reasoning through agentic RAG. We'll also cover tracing and experiment tracking

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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