To ensure reliable performance, it's critical to validate your deep learning system before deployment. Without proper validation, even the most advanced models can produce inconsistent results on the line.
This in-depth white paper walks you through the four essential phases of deep learning validation—providing expert guidance to help you avoid common mistakes, reduce risk, and ensure your application is ready for production.
- Plan your deep learning project by defining goals, evaluating feasibility, and aligning key stakeholders
- Collect and label image data for accurate training – one of the most important steps in model development
- Optimize your system by refining ground truth, adjusting parameters, and retraining your neural network
- Execute Factory Acceptance Testing (FAT) with statistical analysis and repeatability checks to ensure deployment readiness
Download the white paper now to learn how to validate your deep learning application with confidence, and set the foundation for successful, scalable AI deployment on the factory floor.
