Iceberg, MCP, and MLOps: Bridging the gaps for Enterprise

Effective ML orchestration is the foundation of successful enterprise AI systems, connecting data processing, training, and deployment into reproducible workflows. This session explores how ZenML provides the critical pipeline infrastructure that enables teams to standardize their ML processes while maintaining flexibility.

For years, feature platforms like Featureform have powered classical ML systems—serving features to models, productionizing transformations, and helping ML teams scale. But the rise of LLMs and agentic workflows is fundamentally expanding the surface area of the ML platform, introducing new patterns for how data is consumed and acted on. In this session, we’ll explore the next evolution of the feature platform: one that supports both real-time and batch pipelines, bridges traditional ML and agentic systems, and makes data accessible through interfaces like MCP.

Join this ML expert-led session to learn the best practices for designing and managing enterprise-grade ML development and production systems at scale. You’ll learn how Snowflake ML makes it easy to rapidly develop and prototype new ML projects, while ensuring production ML systems deploy and operate in a secure, governed manner.