OpenModelZ (MDZ) provides a simple CLI to deploy and manage your machine learning workloads on any cloud or home lab.

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.

You can find the documentation at docs.open.modelz.ai.

First, run pnpm i to install the dependencies.

Then, run pnpm dev to start the development server and visit localhost:3000.