Machine Learning Summit 2025: Applied ML Engineering to GenAI and LLMs
LLMs, Agents, & Real-World ML – 3 Days. 20+ Experts. 100% Practical
Join 25+ top AI, ML leaders delivering over 20 sessions across 3 tracks.
Track 1: Agents and GenAI in Action
Build intelligent, agentic systems that reason, retrieve, and respond — in production.
Explore how modern AI agents are evolving beyond prompts, how open-source tools help scale them, and what it takes to deploy cognitive workflows in real-world environments. From graph-powered retrieval pipelines to multimodal systems that blend vision, language, and audio — this track is about putting GenAI to work.
For: ML engineers, applied researchers, and AI builders exploring agents, LLM orchestration, and GenAI-powered systems.
Track 2: Applied ML and Improving Model Performance
Engineer explainable, time-aware, and tabular-first ML models that deliver business value.
This track focuses on core ML work: modeling tabular data with modern tools, building interpretable systems, designing decision frameworks under uncertainty, and using time-series techniques like zero-shot forecasting. It bridges classic ML domains and emerging AI-driven techniques—helping you deliver faster, smarter, and more explainable models.
For: Data scientists, ML engineers, and tech leads focused on explainability, real-world performance, and applied ML strategy.
Track 3: Production-Ready ML Systems
Scale, monitor, and maintain reliable ML & LLM-powered products in production.
This track tackles the full lifecycle: monitoring drift, debugging live systems, designing personalized pipelines, and making edge deployments work. Learn how to reduce failure modes, ship faster with MLOps best practices, and deliver consistent model performance with modern infra and observability stacks.
For: MLOps professionals, infra engineers, and AI platform teams running models in the wild.
LLMs, Agents, & Real-World ML – 3 Days. 20+ Experts. 100% Practical
Join 25+ top AI, ML leaders delivering over 20 sessions across 3 tracks.
Track 1: Agents and GenAI in Action
Build intelligent, agentic systems that reason, retrieve, and respond — in production.
Explore how modern AI agents are evolving beyond prompts, how open-source tools help scale them, and what it takes to deploy cognitive workflows in real-world environments. From graph-powered retrieval pipelines to multimodal systems that blend vision, language, and audio — this track is about putting GenAI to work.
For: ML engineers, applied researchers, and AI builders exploring agents, LLM orchestration, and GenAI-powered systems.
Track 2: Applied ML and Improving Model Performance
Engineer explainable, time-aware, and tabular-first ML models that deliver business value.
This track focuses on core ML work: modeling tabular data with modern tools, building interpretable systems, designing decision frameworks under uncertainty, and using time-series techniques like zero-shot forecasting. It bridges classic ML domains and emerging AI-driven techniques—helping you deliver faster, smarter, and more explainable models.
For: Data scientists, ML engineers, and tech leads focused on explainability, real-world performance, and applied ML strategy.
Track 3: Production-Ready ML Systems
Scale, monitor, and maintain reliable ML & LLM-powered products in production.
This track tackles the full lifecycle: monitoring drift, debugging live systems, designing personalized pipelines, and making edge deployments work. Learn how to reduce failure modes, ship faster with MLOps best practices, and deliver consistent model performance with modern infra and observability stacks.
For: MLOps professionals, infra engineers, and AI platform teams running models in the wild.