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MLStacks is a Python package that allows you to quickly spin up MLOps infrastructure using Terraform. It is designed to be used with ZenML, but can be used with any MLOps tool or platform.
Simply write stack and component YAML specification files and deploy them using the MLStacks CLI. MLStacks will take care of the rest. We currently support modular MLOps stacks on AWS, GCP and K3D (for local use).
When we first created ZenML as an extensible MLOps framework for creating portable, production-ready MLOps pipelines, we saw many of our users having to deal with the pain of deploying infrastructure from scratch to run these pipelines. The community consistently asked questions like:
To address these questions, the ZenML team presents you a series of Terraform-based stacks to quickly provision popular combinations of MLOps tools. These stacks will be useful for you if:
🔥 Do you use these tools or do you want to add one to your MLOps stack? At ZenML, we are looking for design partnerships and collaboration to implement and develop these MLOps stacks in a real-world setting.
If you'd like to learn more, please join our Slack and leave us a message!
Thank you to the folks over at Fuzzy Labs for their support and contributions to this repository. Also many thanks to Ali Abbas Jaffri for several stimulating discussions around the architecture of this project.
We'd also like to acknowledge some of the cool inspirations for this project:
FAQs
MLStacks MLStacks.
We found that mlstacks demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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