Machine Learning in Energy: Forecasting, MLOps, and Business Impact
•
Jean-Michel Begon & Kris Peeters
Inside Luminus: how machine learning models move from experimentation to production to forecast electricity demand.
Welcome to a new episode of The Data Playbook Podcast by Dataminded.
In this episode, Kris Peeters talks with Jean-Michel Begon, Senior Machine Learning Engineer at Luminus, about what it really takes to build, deploy, and maintain machine learning models in a business-critical environment.
Together, they unpack how ML teams in the energy sector move from ideation to experimentation, industrialisation, and monitoring, and why production-ready machine learning is about much more than model accuracy alone.
In this episode, you’ll learn:
How Luminus uses machine learning for electricity consumption forecasting
What a practical ML lifecycle looks like in a business team
How to balance experimentation, standardisation, and production delivery
Why GenAI and LLMs are useful tools, but not a replacement for engineering discipline
Latest
Data Contracts and Data Products: What AI Agents Need to Access Enterprise Data
AI agents need their own access rules. Simon Harrer, Entropy Data, on data contracts, data products and purpose-based access control.
Data Mesh After the Hype: Why Data Products Matter More Than Ever
Why Data Products outlasted Data Mesh, where Data Contracts fit, and how AI is changing modern data architecture.
Why AI Agents Need Knowledge Graphs, Not Just Data
Very few companies manage 10,000 of anything. Eric Broda on scaling AI agents like data mesh scaled data products.
