MLRun can run as easily on Amazon SageMaker as it does on a local computer. In fact, it is environment-agnostic. For AWS users, the easiest way to install MLRun is to use a native AWS deployment. Here's how to do that.
MLRun operates with a server side and a client side. The client side can run on everything, including AWS SageMaker, Azure ML, any Python, any Notebook, and more. MLRun can also run on Kubernetes Minikube, with containers, with Docker Compose, on EKS, on a cloud Kubernetes environment, and more.
The only step required is to run
pip install and configure environment variables. MLRun turns the requirements into a server function that uses auxiliary services.
For running MLRun with Amazon SageMaker, MLRun provides two options:
- Using MLRun against workloads that will run on Amazon EKS
- For building a workflow around SageMaker services like SageMaker Autopilot.
For more on all the ways Iguazio works together with AWS, including solution briefs, demos and more, check out the partner page.