Aqueduct
An open-source MLOps framework for defining and deploying machine learning and LLM workloads across any cloud infrastructure.
What is Aqueduct?
Aqueduct is an open-source MLOps framework that enables data scientists and engineers to define machine learning and LLM workflows in vanilla Python and deploy them on any cloud infrastructure, such as Kubernetes, Spark, or AWS Lambda. It addresses the fragmented nature of MLOps by providing a unified interface to existing tools while ensuring centralized visibility into pipeline execution. The framework allows seamless movement of code between different cloud layers without requiring a rip-and-replace approach.
Data scientists and ML engineers who need to deploy and manage machine learning or LLM workflows across diverse cloud infrastructure like Kubernetes, Spark, Airflow, or AWS Lambda. It is suited for teams dealing with the complexity of siloed MLOps tools and seeking a Python-native solution.
Developers choose Aqueduct because it offers a Python-native API without DSLs or YAML configurations, enabling quick production deployment. Its unique selling point is the ability to integrate with and orchestrate workflows across multiple existing cloud infrastructure systems while providing centralized visibility into code, data, and metadata for reliability and debugging.
Overview
Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.
Use Cases
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