Case Study: a grid intelligence startup achieves scalable, cost-efficient MLOps with Agmis

Grid Intelligence Startup reduces ML costs and scales 500,000+ pole assessments with Agmis

A grid intelligence startup, which uses AI to assess utility infrastructure, faced challenges scaling its machine learning operations. Its model development was inefficient and confined to Jupyter notebooks, creating reproducibility issues, data handling gaps, and monitoring blind spots that hindered growth. Agmis partnered with the startup to implement a production-grade MLOps strategy.

Agnis migrated the startup's training to AWS SageMaker and integrated Weights & Biases for experiment tracking, while also logging all training data to Amazon RedShift for complete reproducibility. This solution systematized workflows and transferred deep expertise. The results included a significant reduction in compute costs, the assessment of over 500,000 utility poles, and the creation of a scalable, sustainable ML practice for the client.

Grid Intelligence Startup