Orchestration built for modern machine learning teams
Aqueduct’s API allows you to define your workflows in vanilla Python, so you can get code into production quickly and effectively. No more DSLs or YAML configs to worry about.
Workflows defined in Aqueduct can run on any cloud infrastructure you use, like Kubernetes, Spark, Airflow, or AWS Lambda. You can get all the benefits of Aqueduct without having to rip-and-replace your existing tooling.
Once your workflows are in production, you need to know what’s running, whether it’s working, and when it breaks. Aqueduct gives you visibility into what code, data, metrics, and metadata are generated by each workflow run, so you can have confidence that your pipelines work as expected — and know immediately when they don’t.
Aqueduct is fully open-source and runs in any Unix environment. It runs entirely in your cloud and on your infrastructure, so you can be confident that no data, code, or metadata is ever leaving your cloud environment.
See Aqueduct in action
What others are saying about us
Aqueduct gives me a comprehensive view of the data flow in my ML pipelines. Right now, this context is scattered across a notebook and a couple Miro boards, but these pipelines change so fast that it's hard to keep track of them. To see all of my pipelines end-to- end and to see everything light up green is going to give me the confidence that I need to know everything's working and how well it's working.
"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
Aqueduct gets rid of tedious infrastructure management, so you can take your code from development to production instantly. Once pipelines are in production, Aqueduct’s metrics, checks, and error logs make monitoring pipelines, detecting issues, and debugging errors simple.
Data &
ML Engineers
Aqueduct brings software discipline to your machine learning infrastructure. Our cloud-, vendor-, and library- agnostic API lets you use deploy pipelines on your existing infrastructure while versioning code & data together and ensuring ongoing quality — rest easy knowing that pipelines or working as expected and that you’ll be immediately notified when things break.