May 1, 2021
Failing fast is one of Agile development’s conceptual pillars. Embracing the principle of failing fast leads to lower cost of failure, accelerated learning, and innovation-driven organizational culture. In the AI & machine learning world however, where testing & fast failing of models is heavily dependent on access to computing power, these desirable benefits are often out of reach. If your employer is computationally affluent like Facebook or Google, your machine learning models enjoy the best environment to fail fast & get rapidly fine-tuned towards a minimum viable product. If your employer doesn’t have those resources, then not so much.
William Falcon wants to remedy that inequality with a Training-at-Scale system that compresses research & testing from months into days. Expediting that process not only gets machine learning models into production faster, but can also accelerate an organization’s journey towards digital transformation. We talk with William about how his startup Grid.ai will provide this “superpower” and how doing so can provide enterprises with a competitive advantage.