Investing in Innovation
Deep learning's journey from theoretical to commercial viability highlights the need for robust infrastructure, including accelerators and laboratories, to support innovation. With significant capital investments required for both training and deploying models, the market is witnessing a shift where companies are increasingly utilizing GPUs for inference, demonstrating a growing appetite for practical applications of AI technology.In this clip
From this podcast
The Hard Part with Evan McCann
Gennady Pekhimenko from CentML
Related Questions
Where do companies spend money on AI in the episode Gennady Pekhimenko from CentML and the clip Investing in Innovation?
Do companies need large machine learning teams as discussed in the episode Gennady Pekhimenko from CentML and the clip Talent and Innovation?
Why are deep learning models so expensive that only big players in tech can afford to develop them?