Our deep learning algorithms automate the data labeling process, reducing time and cost. They interpret and rapidly auto-label hundreds of thousands of images to optimize and initiate labeling-dependent actions for AI systems.

We utilize Azure AutoML Pipeline to connect LabelOps and MLOps into a single workflow. High quality data is secured over time through continuous training within the project, reducing time and cost.

This feature provides a workspace to input LiDAR data, collected through self-driving data collection vehicles, into a 3D vector space for better visualization of the data and to easily and quickly label this data. Automated 3D object detection with deep learning saves on time and cost. The LiDAR Cuboid also creates sensor-fusion data using LiDAR to Camera or Camera to LiDAR through its parameter calibration function.

Labeled data can be filtered by instance to speed up inspection and edge-case data curation.

Multiple AI models and humans evaluate one dataset together and statistically detect abnormalities in the data, which effectively screens data at the inspection stage. Because AI models can evaluate the data based on different inspection criteria, data quality can be quickly judged from various perspectives.

This feature provides all the functionality needed to manage massive datasets and incorporates smart, automated labeling for large volumes of data. The LabelOps function maximizes data labeling efficiency from Smart Labeling, Model Learning and Output Conversion to budget management, resulting in reduced cost and maximising time-efficiency.

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