OpenModelZ
Simplify machine learning deployment for any environment.
OpenModelZ (MDZ) provides a simple CLI to deploy and manage your machine learning workloads on any cloud or home lab.
Why use OpenModelZ 🙋
OpenModelZ is the ideal solution for practitioners who want to quickly deploy their machine learning models to a (public or private) endpoint without the hassle of spending excessive time, money, and effort to figure out the entire end-to-end process.
We created OpenModelZ in response to the difficulties of finding a simple, cost-effective way to get models into production fast. Traditional deployment methods can be complex and time-consuming, requiring significant effort and resources to get models up and running.
- Kubernetes: Setting up and maintaining Kubernetes and Kubeflow can be challenging due to their technical complexity. Data scientists spend significant time configuring and debugging infrastructure instead of focusing on model development.
- Managed services: Alternatively, using a managed service like AWS SageMaker can be expensive and inflexible, limiting the ability to customize deployment options.
- Virtual machines: As an alternative, setting up a cloud VM-based solution requires learning complex infrastructure concepts like load balancers, ingress controllers, and other components. This takes a lot of specialized knowledge and resources.
With OpenModelZ, we take care of the underlying technical details for you, and provide a simple and easy-to-use CLI to deploy your models to any cloud (GCP, AWS, or others), your home lab, or even a single machine.
You could start from a single machine and scale it up to a cluster of machines without any hassle. OpenModelZ lies at the heart of our ModelZ, which is a serverless inference platform. It's used in production to deploy models for our customers.
Documentation 📝
You can find the documentation at docs.open.modelz.ai.
Local Development
First, run pnpm i to install the dependencies.
Then, run pnpm dev to start the development server and visit localhost:3000.