A deep dive into data engineering, cloud infrastructure, and production data science.
Predictions are just data
In conversations with over 200 data teams, we've found that the "typical" solution to ML model deployments is wrong. Most people don't need REST endpoints — they just need predictions as data.
Aqueduct v0.1: A simpler way to run data science workflows
We're excited to share Aqueduct v0.1. Aqueduct allows you to easily construct robust data & ML pipelines that work with your cloud infrastructure.
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.
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.
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.