Edge MLOps posts

As industries seek faster insights and real-time responsiveness, Edge MLOps (Machine Learning Operations at the Edge) is emerging as a game-changer. Unlike traditional MLOps that rely on centralized cloud infrastructure, Edge MLOps enables machine learning models to be deployed, monitored, and retrained close to the data source.

This article takes you inside a real-world deployment where MLOps at the edge powers hourly solar radiation forecasts in a photovoltaic plant. By combining AI with real-time data flows, and cloud integration, the solution delivers higher prediction accuracy, faster response times, and optimized plant performance. Discover how Edge MLOps is transforming solar forecasting.

For industrial companies embracing AI, understanding the MLOps workflow is key to turning use cases into real-world results. In this post, we show how Barbara integrates seamlessly into each stage of the workflow especially where the edge plays a critical role.

We are still at the dawn of Machine Learning and Artificial Intelligence in the Industry. Still, as we envision new use cases and develop them in our environment, we realise success in the future depends on the proper implementation today.