Creating an ML model is just a starting point. To bring the technology into production service, you need to solve various real-world issues such as building a data pipeline for continuous training, automated validation of the model, version control of the model, scalable serving infra, and ongoing operation of the ML infra with monitoring and alerting. In this session, we will learn concepts of ""ML Ops"" (DevOps for ML) and how to combine Argo, Kubeflow, and cloud services to build a production ML service infra.
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re_ty: Publish; product: Cloud - AI and Machine Learning - TensorFlow Enterprise; fullname: Kaz Sato; event: Google Cloud Next 2018;
