ANDY HOCK
VP of PRODUCT
Cerebras
Presentation Title:
How to Train Larger Models to Higher Accuracy Using Less Compute
Presentation Summary:
ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of scale that bring greater component reuse while improving efficiency of system development and maintenance. For an end-to-end ML platform with broad adoption, scaling relies on pervasive ML automation and system integration to reach the quality we term self-serve; a quality we define with ten requirements and six optional capabilities.
With this in mind, we identify long-term goals for platform development, discuss related tradeoffs and future work. Our reasoning is illustrated on two commercially-deployed end-to-end ML platforms that host hundreds of real-time use cases at Meta — one general-purpose and one specialized.
about | Andy Hock
Dr Andy Hock is VP of Product at Cerebras. Andy came to Cerebras from Google, where he led Data and Analytics Product for the Terra Bella project, using deep learning and AI to create useful data for maps and enterprise applications from satellite imagery.