 role that a model deployer plays in a ZenML StackSeamless Model Deployment: Facilitates the deployment of machine learning models to various serving environments, such as local servers, Kubernetes clusters, or cloud platforms, ensuring that models can be deployed and managed efficiently in accordance with the specific requirements of the serving infrastructure by holds all the stack-related configuration attributes required to interact with the remote model serving tool, service, or platform (e.g. hostnames, URLs, references to credentials, and other client-related configuration parameters). The following are examples of configuring the MLflow and Seldon Core Model Deployers and registering them as a Stack component:Copyzenml integration install mlflow
zenml model-deployer register mlflow --flavor=mlflow
zenml stack register local_with_mlflow -m default -a default -o default -d mlflow --setCopyzenml integration install seldon
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
...
zenml stack register seldon_stack -m default -a aws -o default -d seldon

Lifecycle Management: Provides mechanisms for comprehensive lifecycle management of model servers, including the ability to start, stop, and delete model servers, as well as to update existing servers with new model versions, thereby optimizing resource utilization and facilitating continuous delivery of model updates. Some core methods that can be used to interact with the remote model server include: