#227 · Primary category： MLOps & Evaluation
aqueduct
Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.
Project last updated：06/07/23
GitHub Stars
517
Forks
20
Contributors
19
License
Apache-2.0
Why we included this project
Aqueduct attacks a familiar problem: ML code runs happily in a notebook, but moving it to real infrastructure usually means rewriting it and losing sight of how it performs once deployed. The framework lets you define ML and LLM workloads in plain Python and push them to Kubernetes, AWS, GCP, or similar backends without switching languages, while keeping execution and model metrics visible after launch. Development stopped in 2023, so this is not a tool to build a long-term production system on. Its real value today is as a study piece: a readable example of how an MLOps layer abstracts compute, tracks runs, and ties into existing cloud stacks, which engineers can mine for ideas when building their own tooling.
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