How Voxel cut model retraining time by 80%

Voxel has significantly enhanced its machine learning operations by integrating Sematic, a comprehensive ML orchestration platform, into its workflow. This integration has enabled Voxel to drastically cut model retraining times from weeks to days and achieve significant cost savings, all while improving productivity by 80%. Sematic's features such as traceability, reproducibility, and observability have streamlined Voxel's ML processes, making debugging easier and faster. The decision to adopt Sematic was driven by its ability to provide fine-grained control of compute resources and its seamless integration with Voxel's existing Python-centric stack, offering superior capabilities compared to alternatives like Airflow and Kubeflow. This strategic move has allowed Voxel to shift focus from maintaining an internal orchestration tool to advancing high-priority initiatives, thereby elevating safety standards in industrial warehouse operations for clients like Office Depot and Dollar Tree. As Voxel continues to grow, Sematic is expected to further empower its team to operate efficiently across local and cloud infrastructures, supporting their mission towards achieving a world with zero injuries through advanced computer vision models.