Case Study: TotalEnergies achieves faster anomaly detection with HumanSignal's Label Studio

TotalEnergies reduces time to market with HumanSignal using Docker-based anomaly detection pipelines

TotalEnergies, a global energy company, faced the challenge of predicting and preventing critical equipment failures on its oil rigs. To build an effective anomaly detection model, they needed to leverage the knowledge of field experts through data annotation and integrate this into their MLOps strategy. They turned to HumanSignal and implemented their product, Label Studio Enterprise, to create an efficient and scalable annotation solution.

HumanSignal's solution enabled TotalEnergies to deploy Label Studio Enterprise via Docker for easier integration and improved security. The platform supported semi-supervised and active learning workflows, allowing field experts to annotate data effectively. This resulted in a cutting-edge tool for engineers and a significant reduction in the time to market for their overall MLOps strategy. The user-friendly interface and robust user management features provided by HumanSignal also ensured a high quality of annotated data for model training.

TotalEnergies