What exactly do we mean by “AI project failure?” It’s indeed a fine line, because data practitioners should never feel discouraged from exploring the unknown. In that vein, failure is a critical element of advanced development and good data science. In the industry, viewing “failure” as a fundamental flaw in doing data science could create the wrong expectations for practitioners.
In this fireside chat featuring Will Benton, Principal Product Architect at NVIDIA, uncover how we can encourage this type of experimentation while also empowering practitioners (on both technical and business teams) with the tools needed to avoid AI project failure at large (i.e., sound and reliable data, AI Governance and MLOps, collaboration with IT).