If you are wondering how to bring your on-prem ML workloads to a scalable, portable, composable and secure production platform, the open source Kubeflow pipelines is your answer. We demonstrate an easy step by step process with ML models built with scikit-learn, xgboost and tensorflow ML frameworks.
We will show how to create an end to end ML pipeline on the Google Cloud including data prep, hyperparameter tuning, model training, model deployment, prediction, explanation and training orchestration. The solution can be extended to the Anthos
framework for a full multi-cloud deployment.
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