Introducing mlinfra — a hassle free way to deploy ML Infrastructure
I’m really excited to be launching mlinfra, a python package designed to streamline the deployment of various MLOps tools within an MLOps stack, quickly and with best practices. The core philosophy behind mlinfra is to simplify and expedite the deployment of MLOps infrastructure. This approach enables ML Engineers or ML Platform engineers to concentrate on delivering business value, by significantly reducing the time and resources typically required for deploying MLOps tools on the cloud.
As an ML Engineer, I well understand the complexities and challenges we face in deploying and configuring machine learning infrastructure. The traditional approach to deploying ML infrastructure often involves tedious, repetitive tasks such as writing complex terraform logic, leaving less time for the critical aspects of ML development. This is where mlinfra steps in. It’s designed to automate and streamline the deployment of various MLOps tools within a highly configurable MLOps stack, ensuring rapid deployment aligned with best practices.
The fundamental concept behind mlinfra is to establish a universal Infrastructure as Code (IaC) framework that expedites the creation and deployment of MLOps stacks. This package empowers MLOps Engineers and Platform Engineers to deploy a variety of MLOps tools across different stages of the machine learning lifecycle. The essence of mlinfra lies in its Python layer, which interprets the deployment configuration and utilizes Terraform modules to deploy these tools. It further enhances the process by dynamically generating a suite of inputs, roles, and permissions, thereby simplifying and streamlining the deployment process.
The heart of mlinfra lies in its core philosophy: simplify and accelerate the deployment of MLOps infrastructure to the cloud. This means you, as an engineer, can focus more on delivering robust business logic and less on the nuances of deployment. Developed from a personal need and countless hours of dedication, mlinfra offers a sophisticated, yet incredibly user-friendly approach to deploying MLOps stacks in the cloud.
✨ Features that set mlinfra apart:
- A lightweight Python package with intrinsic logic for cloud infrastructure deployment.
- built on top of battle hardened renowned terraform modules like terraform-aws-modules by Anton Babenko.
- Cloud-agnostic design, enabling the effortless deployment of any MLOps lifecycle tool across various cloud platforms.
Whether you’re an experienced ML engineer or a tech enthusiast, your input and support can significantly impact the development of mlinfra.
Using mlinfra is really straightforward as well; create a Python virtual environment, install the tool, and explore the example stacks available on our website.
python -m pip install mlinfraFor more information, dive into our documentation. Feel free to reach out directly for any queries or suggestions.
PS: Do give the project some support by sharing it with people working in MLOps domain and giving it a star!
