Aqueduct: Taking Data Science to Production

As ML has become widely adopted, the next critical challenge for data teams is in generating value from data science & machine learning. Production data science infrastructure is the missing link that will enable data science and machine learning to succeed, by abstracting away low-level cloud infrastructure. Aqueduct is the world's first production data science platform; it enables data scientists to run models anywhere, publish predictions everywhere, and ensure prediction quality.

Continue reading →

MLOps: Right Problem, Wrong Solution

The fundamental problem with MLOps is that it mixes together tools for two very different concerns — (1) ensuring high-quality predictions and (2) deploying & managing cloud infrastructure. As a consequence, this requires data teams to have expertise in both data science and also in low-level cloud infrastructure.

Continue reading →

The Real Challenge in (Useful) Machine Learning isn’t Learning

This post discusses research from the UC Berkeley RISE Lab around building scalable prediction infrastructure, and why that wasn't the problem the world needed solved.

Continue reading →

How Machine Learning Became Useful

This post explores how big data, advances in parallel computing, and new abstractions transformed machine learning and artificial intelligence.

Continue reading →