The competitive advantage of computer vision companies is no longer in model architectures but in their training data and the ML-Ops behind it. How do AI-first companies compete?
We'll explore a number of emerging computer vision cases where models become fixed elements, re-trained on continuously evolving datasets as a company's deployments grow. This calls for the need for a CRM-like experience for training data, where ML-Ops tools can apply changes from multiple sources, and enabling complex labeling or inference workflows to occur.
We'll talk about how V7 has tackled this problem, what the needs for the ML-Ops community are, and how to standardize our work to enable further collaboration.