MLOps and Real-World Production Experience with Snowflake Intelligence

MLOps on Snowflake at Romande Energie: Industrialising Energy Forecasting Models
Today’s volatility in the energy market puts significant pressure on energy providers, and Romande Energie is no exception. By continuously refining our machine learning models that forecast our customers’ electricity production and consumption, we can optimise our purchasing decisions on energy trading markets.
However, without robust MLOps practices, regularly evolving ML models are difficult to deploy reliably into production. In this session, we will show how we combined GitLab pipelines with Snowflake’s machine learning capabilities to establish a smooth, efficient, and industrial-grade process for deploying and operating ML models at scale.
Sébastien Jeanfavre, Data Engineer - Romande Energie

Snowflake Intelligence in Production at Swissquote
This session shares a real-world production experience with Snowflake Intelligence, from initial rollout to day-to-day operational usage.
We will cover the architecture choices, integration patterns, and governance considerations that enabled a scalable deployment. Attendees will gain insights into performance optimization, security challenges, and user adoption lessons learned in production environments. The presentation also highlights practical use cases where Snowflake Intelligence delivered measurable business value and operational efficiency.
Finally, we will discuss key recommendations, pitfalls to avoid, and best practices for successfully industrializing AI-driven analytics on Snowflake.
Milton Marques Pereira*, Data & Analytics Engineer - Swissquote*
Brenda Taboga*, Data & Analytics Specialist - Swissquote*

Agenda
17:00-17:30 - Welcome
17:30-18:00 - MLOps on Snowflake at Romande Energie: Industrialising Energy Forecasting Models
18:00-18:30 - Snowflake Intelligence in Production at Swissquote
18:30-21:00 - Apéritif & Networking

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