Energy flexibility is playing an increasingly fundamental role in the UK energy market. With the adoption of renewable energy sources such as EVs, solar panels and domestic and commercial batteries, the number of flexible assets is soaring - making aggregation and flexibility trading infinitely more complex and requiring vast amounts of data modelling and forecasting. To address this challenge, Flexitricity adopted MLOps best practices to tackle this complex real-world challenge and meet the needs of the scaling energy demand in the UK. 

The session will cover:

- The complex technical challenge of energy flexibility in 2025.

- The critical requirement to invest in technology and skillsets.

- A real-life view of how machine learning operations (MLOps) scaled Flexitricity’s data science model development.

- How innovations in technology can support and optimise delivering on energy flexibility. 

The audience will gain insight into:

- The challenge of building data science models to keep up with scaling demand.

- How MLOps best practices can be adopted to drive efficiency and increase data science experiments to 10000+ per year.

- Lessons learned from adopting MLOps pipelines.

Modern enterprises can’t manage data they don’t understand – uncovering the code-to-data relationship is the missing link. As data ecosystems grow more complex, traditional approaches to tracking data lineage can’t keep up. This talk explores how AI-driven code analysis can automatically build end-to-end lineage graphs, giving engineers clear visibility into hidden dependencies across large, legacy, and regulated systems. We’ll show how AI enhances data catalogues and introduce Gable - a tool that helps teams map, validate, and monitor data flows at scale. A live demo on a large energy data codebase will highlight how AI transforms lineage tracking from a manual headache into an automated, scalable solution.