In this developer-focused session, Seldon explores how to deploy, optimize, and manage LLMs and other foundation models in real-world environments. He demystifies common deployment strategies – batch, online, streaming, edge, embedded – and dives deep into the tooling and infrastructure needed to balance performance, cost, and control. With examples from industries like finance, health, and education, this session bridges foundational AI concepts with hands-on tactics using Seldon Core, MLServer, and the new LLM module.
About
Learnings
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Foundation Model Fundamentals: Learn how LLMs differ from traditional ML, with zero-shot/few-shot learning, human feedback alignment, and real-world examples across marketing, healthcare, and finance.
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Challenges of Scaling LLMs: Understand practical issues like GPU shortages, cost unpredictability, model hallucinations, and infrastructure constraints for production deployment.
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Seldon’s LLM Module: Explore flexible pipelines that combine proprietary and open models, with advanced optimizations like quantization, multi-GPU serving, continuous batching, and key-value caching.
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Developer Tooling & Monitoring: Get visibility into prompt logs, model comparison, usage metrics, and versioning with Seldon’s enterprise-grade UI and integrations.
Ramon Perez
Ramon Perez has spent the past nine years working across the data spectrum spanning data analysis, building dashboards, developing data platforms, and creating numerous machine learning models. Beyond his technical work, Ramon is passionate about sharing knowledge, having taught and presented at universities, companies, conferences, and MeetUp events. When he’s not immersed in data, you’ll likely find him mountain biking, exploring the outdoors, or discovering new coffee spots and eateries.
Complex, real-time use cases is what we do best
Talk with a expert to explore how Seldon can support more streamlined deployments for real-time, complex projects like fraud detection, personalization, and so much more.
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