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Seamless End-to-End Production Machine Learning with Seldon and MLFlow
Seamless End-to-End Production Machine Learning with Seldon and MLFlow

Seamless End-to-End Production Machine Learning with Seldon and MLFlow

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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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