Finding it difficult to maintain existing models? MLOps helps to deploy, monitor, and manage ML production with better accuracy and speedy development. How can the operations be achieved? Machine Learning Operations (MLOps) are achievable through Kubeflow as it helps with the scalable and portable deployment of ML workflows. In this webinar, learn about model maintenance, deployment, automation, and more through Kubeflow.

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Discussion Topics:

Overview of Kubeflow MLOps platform
End-to-End Model Deployment through Jupyter Notebooks on Kubeflow
Docker Image Creation using Fairing on Kubeflow
Model Deployment using KFServing on Kubeflow
Model Monitoring and Visualization using Tensorboard and Prometheus/Grafana on Kubeflow
Model Training & Serving Pipeline using Argo on Kubeflow
Jupyter Notebook to Argo Pipeline using Kale on Kubeflow
Q & A