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Feature Pipelines on Aqueduct

Build features that your whole team can use, and share them on your existing infrastructure.

Flexible, repeatable feature pipelines.

Highly-collaaborative teams require shared abstractions to share features, but modern feature stores are often overkill. With Aqueduct, you can define shared, repeatable feature pipelines in vanilla Python code and publish your features in any database.

Your model training or inference pipelines can seamlessly reuse these features from any databasea and on any cloud infrastructure.

MLOps reimagined for 

modern ML teams


Define your ML tasks in vanilla Python — no more YAML configs, Dockerfiles, or DSLs to worry about.

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Integrated with your cloud

Aqueduct workflows can run on any cloud infrastructure you already use — choose from Kubernetes, Spark, Airflow, Lambda, or Databricks.

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Native LLM support

Integrate powerful LLMs into your pipelines with a single API call — all without any operational overhead.

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Deep visibility into data & code

Regardless of where your code is running, Aqueduct captures and validates the code and data at every stage, so you know what ran and when it ran.

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Easy to debug

Every function run has error logs and stack traces, so you can pinpoint errors quickly.

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Runs securely in your cloud

Aqueduct is fully open-source, so you can be sure your code and data is always where it's supposed to be.

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What our users are saying

Jack Reynolds

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.

Pablo Vega-Behar

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.

Anchit Desai

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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See how Aqueduct can help untangle the MLOps Knot.

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