 story of a remote stack.

Model Deployers FlavorsZenML comes with a local MLflow model deployer which is a simple model deployer that deploys models to a local MLflow server. Additional model deployers that can be used to deploy models on production environments are provided by integrations:

Model Deployer Flavor Integration Notes MLflow mlflow mlflow Deploys ML Model locally BentoML bentoml bentoml Build and Deploy ML models locally or for production grade (Cloud, K8s) Seldon Core seldon seldon Core Built on top of Kubernetes to deploy models for production grade environment Hugging Face huggingface huggingface Deploys ML model on Hugging Face Inference Endpoints Custom Implementation custom Extend the Artifact Store abstraction and provide your own implementation

Every model deployer may have different attributes that must be configured in order to interact with the model serving tool, framework, or platform (e.g. hostnames, URLs, references to credentials, and other client-related configuration parameters). The following example shows the configuration of the MLflow and Seldon Core model deployers:

# Configure MLflow model deployer

zenml model-deployer register mlflow --flavor=mlflow

# Configure Seldon Core model deployer

zenml model-deployer register seldon --flavor=seldon \

--kubernetes_context=zenml-eks --kubernetes_namespace=zenml-workloads \

--base_url=http://abb84c444c7804aa98fc8c097896479d-377673393.us-east-1.elb.amazonaws.com

...

The role that a model deployer plays in a ZenML Stack