Case Study: Embark achieves 5-day turnaround and 99%+ training-data quality with Scale AI

Scaling ground-truth perception data with Scale

Embark, a San Francisco–based self-driving truck company, needed to scale its training data pipelines for sensor fusion and 3D labeling after in-house annotation proved costly, slow and error-prone. To focus on developing autonomous trucks rather than labeling operations, Embark turned to Scale for managed training data services, including Sensor Fusion Cuboids and related segmentation products.

Scale provided a blended human-and-ML labeling platform and operational support that cut dataset turn-around to five business days and raised data quality to over 99%, giving Embark faster iteration, greater confidence in training data, and responsive engineering support (often resolving questions within hours). Scale’s managed solution and tight communication channels (e.g., Slack) became a critical part of Embark’s stack for LIDAR and camera labeling.

Embark

Brandon Moak

Chief Technology Officer