Model Deployment

SynapseML provides distributed machine learning algorithms for Spark through its Python library.

Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.

Eurybia tracks model drift and validates data during machine learning model deployment.

Package, configure, and iterate on a model with Truss at whatever level of control your model needs.

MMDeploy provides a model deployment framework from the OpenMMLab project.

FedML supports distributed model training, model serving, and federated learning, with a scheduler for AI jobs across cloud and local clusters.

GraphPipe provides a protocol and software for deploying machine learning models separately from their frameworks.