Tackle error-prone and slow manual data labeling

One of the most critical phases in VA model building is the data labeling phase where the data sets are tagged manually. It is the base for iterative learning and is essential for the training, validation, and tuning of deep learning modelS. But there are many challenges here - manual labeling is error-prone, dependent on the quality of the work done by the labeling team and the domain knowledge of each team member that determines the accuracy. It is also a very time-consuming process. All this time and effort translates to escalating costs in training ML models. Apexon’s image and video analytics solutions help in the end-to-end data preparation cycle, from collecting the data to design and deployment. Our team of experts in Image Processing and Computer Vision has the technical capability and domain understanding to build production-grade models and deploy them for various use cases. We help you cover your entire gamut of data with our unique accelerators, pre-built solutions, proprietary AI models, and scalable analysis, to improve your efficiency and productivity.