With Aqueduct, you can construct prediction pipelines as compositions of simple Python functions. Pull data in from one or more data sources, generate predictions, and validate them — all in vanilla Python.
Once your pipelines are defined, you can run them anywhere — on your laptop or in the cloud — without changing any code. No need to learn any new DSLs or write any YAML configs.
Define your workflows in vanilla Python — no more YAML configs, Dockerfiles, or DSLs to worry about.
Aqueduct workflows can run on any cloud infrastructure you already use — choose from Kubernetes, Spark, Airflow, Lambda, or Databricks.
Regardless of where your code is running, Aqueduct captures the code and data at every stage, so you know what ran and when it ran.
Metrics and checks mean you can measure your ML pipelines, know when things are headed in the wrong direction, and act quickly.
Every function run has error logs and stack traces, so you can pinpoint errors quickly.
Aqueduct is fully open-source, so you can be sure your code and data is always where it's supposed to be.
Machine Learning Engineer
Aqueduct gives me a comprehensive view of the data flow in my ML pipelines. Today, 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.
Director of Data Science, Sparks & Honey
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.
Lead Engineer, Replate
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 into production. We can focus on delivering better models rather than maintaining cloud infrastructure.
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