Computer vision in AI: The data needed to succeed

Developing the capacity to annotate massive volumes of data while maintaining quality is a function of the model development lifecycle that enterprises often underestimate. It's resource intensive and requires specialized expertise. At the heart of any successful machine learning/artificial intelligence (ML/AI) initiative is a commitment to high-quality training data and a pathway to quality data that is proven and well-defined. Without this quality data pipeline, the initiative is doomed to fail. Computer vision or data science teams often turn to external partners to develop their data training pipeline, and these partnerships drive model performance.