Seamless MLOps with Seldon and MLflow

Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a Kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.
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[@kachrooabhishek]

whats seldon is providing which is not provided by
AIR-FLOW , KUBE-FLOW

[@ChengZhang]

What’s the relationship between seldon and mlflow

[@stanislavg.7903]

mlflow support seldon serving or seldon support mlflow models format.