Machine Learning in Energy: Forecasting, MLOps, and Business Impact - The Data Playbook Podcast with Kris Peeters & Jean-Michel Begon
The Data Playbook Podcast
<p>How do you move machine learning from notebook experiments to production in a real business environment?</p>
<p>In this episode of <strong>The Data Playbook Podcast</strong>, Kris Peeters sits down with <strong>Jean-Michel Begon</strong>, Senior Machine Learning Engineer at <strong>Luminus</strong>, to explore how machine learning models are built and operationalized inside an energy company.</p>
<p>They discuss <strong>electricity demand forecasting</strong>, the <strong>machine learning lifecycle</strong>, model experimentation, industrialisation, monitoring, collaboration with IT, and the role of <strong>GenAI and LLMs</strong> in modern ML teams.</p>
<p>You’ll hear practical lessons on:</p>
<ul>
<li>production machine learning</li>
<li>ML team structure</li>
<li>forecasting model development</li>
<li>data pipelines and platform support</li>
<li>model monitoring and performance review</li>
<li>balancing business value with technical rigor</li>
</ul>
<p>Explore the full podcast series: <a href="https://youtube.com/playlist?list=PLJ_da7qdfL83BFGdNF0CIFB9ygsYPH2Ai&si=4IKtFqBcZhUSg77r" rel="nofollow">The Data Playbook Playlist</a>Discover more podcasts, blogs, and webinars: <a href="https://www.dataminded.com/resources" rel="nofollow">Dataminded Resources</a>Visit the Dataminded website: <a href="https://www.dataminded.com/" rel="nofollow">https://www.dataminded.com/</a></p>