Aqueduct provides the infrastructure needed to connect, deploy, and manage ML in production.
By abstracting away low-level cloud infrastructure, Aqueduct turns the process of deploying models from a days- or weeks-long effort into a single Python API call.
Aqueduct comes with a pre-built suite of connectors to data warehouses, databases, and business systems, allowing you to publish predictions wherever they're needed.
With Aqueduct's checks and metrics, you gain visibility into how well your models are performing on an ongoing basis and get immediate notifications if (when) things break.
Less time managing engineering responsibilities means more time focused on solving business problems using machine learning.
Data engineers no longer need to manage complex & tedious deployment processes in order to enable data scientists to succeed. When things break, better visibility enables quick bugfixes.
Quicker deployment cycles means that the whole data team can have impact throughout the organization on a consistent basis — and quantifiable metrics enable consistent improvement!