Run open LLMs on Aqueduct. Learn more →

The Aqueduct logo.



Seamlessly run
machine learning on 







Aqueduct is an MLOps framework that allows you to define and deploy machine learning and LLM workloads on any cloud infrastructure.
# Use an existing LLM.
vicuna = aq.llm_op('vicuna_7b', engine='eks-us-east-2')
features = vicuna(
        "Turn this log entry into a CSV: {text}" 

# Or write a custom op on your favorite infrastructure!
  # Get a GPU.
  resources={'gpu_resource_name': ''}
def train(featurized_logs):
  return model.train(features) # Train your model.


Integrated with your cloud

MLOps reimagined for 

modern ML teams


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

Learn More →
Integrated with your cloud

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

Learn More →
Native LLM support

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

Learn More →
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.

Learn More →
Easy to debug

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

Learn More →
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.

Learn More →

Trusted by top machine learning teams


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.

Why Aqueduct?

Get more value out of machine learning, faster. With Aqueduct, you can run experiments more quickly, deploy models faster, and debug failures effectively.
Centralize your machine learning code, data, and metadata in a single place. Always know what's running, whether it worked, and who's responsible.
Have confidence that your models and predictions are behaving like they're supposed to, and proactively detect failures before stakeholders and customers complain.
Avoid vendor and cloud lock-in by using general-purpose, system-agnostic APIs. Experiment more nimbly with new tools and infrastructure.
The Aqueduct logo.
Get started with Aqueduct

Fully open-source and easy to setup on your laptop or in your cloud

Try Aqueduct →
The Aqueduct logo.
Check out the code

See how Aqueduct works, make a suggestion, and share your feedback — we'd love to hear from you!

The Slack logo.
Join the community

Discuss MLOps, share feedback, and learn from top ML teams.

Stay up to date with Aqueduct


Why AqueductOpen SourceDocumentationResources



Try Aqueduct today

See how Aqueduct can help untangle the MLOps Knot.

The Aqueduct logo.

© 2023 Aqueduct, Inc. All rights reserved.