Deploying Snorkel-built models to the Databricks Workspace Model Registry and Unity Catalog
Snorkel Flow supports integrations for Databricks Workspace Model Registry and Unity Catalog, which is a unified governance solution for managing data and AI as...
Deploying Snorkel-built models
This page walks through how to deploy a Snorkel-built application, export said deployment, and stand it up in an external production environment for inference a...
Deploying Snorkel-built models to AWS SageMaker
This tutorial walks through the four required steps to deploy a Snorkel-built application to AWS SageMaker:
Deploying Snorkel-built models to Azure Machine Learning
There are three required steps for deploying a Snorkel-built application to Azure Machine Learning (Azure ML):
Deploying Snorkel-built models to Vertex AI
This tutorial walks through the four steps that are required to deploy a Snorkel-built application to Vertex AI:
MLflowModel format: MLflow-compatible application deployments
Application deployments (see Deploying Snorkel-built models) can be packaged in the MLflowModel format. The MLflowModel package format is compatible with the st...