Machine learning infrastructure built for data teams
Seamlessly run machine learning models in the cloud, with nothing more than a couple of lines of Python - and avoid getting caught up in the weeds of repetitive engineering tasks.
Run your pipelines anywhere (locally or in the cloud). You can deploy & update your models on your own infrastructure without building custom Docker containers, managing Kubernetes deployments, or storing passwords in plaintext. With out-of-the-box connectors to data systems, you can also access the freshest data easily & reliably
Using Aqueduct's Metrics and Checks you can leverage your domain expertise to create user defined constraints that run as a part of your workflow, so you can quickly debug & fix errors..
See Aqueduct in action
What others are saying about us
"Aqueduct makes it easy to add a couple decorators to your codebase and automatically capture metrics, track them over time, and enforce constraints on those measurements over time. I don't have to think about where or how I track these things because Aqueduct does it for me."
"Our previous infrastructure was built by data scientists and engineers with little knowledge of each others' best practices. It worked but wasn't ideal for us. Aqueduct streamlines production data science by providing a simple Pythonic API that makes it easy to get models in production. We can focus on delivering better models rather than maintaining cloud infrastructure."
Aqueduct is built for…
Data
Scientists
By abstracting away low-level cloud infrastructure, Aqueduct makes it easier – and quicker - than ever to plug data science into your business.
Data
Leaders
By simplifying ML infrastructure, Aqueduct frees your data scientists and engineers up to focus on the tasks they were hired to do. Get to production ML quickly and friction-free.
Data & ML
Engineers
No more worrying about where data science projects will run in the cloud or who's responsible for getting them there. Aqueduct automates the whole process.