LOGISTICS
Real-time Detection
We automated the detection of incidents on a tugboat. This reduced the time to go through manual incident reports and summarize different situations that could damage an engine. We streamlined and automated the end-to-end machine learning lifecycle, from development and training to deployment and monitoring. This process allows them to add and deploy new models with ease.
Allows business to identify unusual patterns as soon as they occur. Reduces the likelihood of overlooked anomalies.
Timely insights enable quicker responses and help identify potential risks before they escalate.
Ensures models remain accurate and reliable. Allows team to focus on strategic tasks rather than monitoring the data.
Our client lacked visibility into engine anomalies and encountered delays in incident reporting, leading to millions in part replacement costs and vessel downtime. Headstorm developed a machine learning model to detect engine anomalies as well as data drift in engine sensors to proactively identify incidents that could potentially destroy the engine and save significant downtime.
Through the Headstorm Foundry process, we met with different stakeholders to identify and confirm the most feasible business problem to solve. We began with 4 use cases and, based on the business value and data availability, landed on anomaly detection.
We identified the key data attributes related to the use case and validated the relationship on existing data. We then iterated through the process of model selection, experimentation, testing and performance metrics evaluation until we met the agreed evaluations.
We setup Infrastructure as Code (IaC) for resource provisioning to ensure seamless integration of models into production environment. We also set up alerts for pipeline failures and data drifts to highlight the need of adapting the model to new data to ensure the model remains effective in capturing the evolving pattern of data.
The stakeholder was satisfied with the deployment of the models in production and the automated detection of different incidents along with data drift in the sensor. Ability to add additional model with ease to MLOps pipeline was a bonus.
Dallas
15455 Dallas Pkwy, Ste 300
Addison, TX 75001
214.396.5390
Pittsburgh
181 42nd St.
Pittsburgh, PA 15201
San José
SIGMA Business Center
San José, San Pedro
Engineering Growth™
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