Creating an ML model is just a starting point. To bring it into production, you need to solve various real-world issues, such as building a pipeline for continuous training, automated validation of the model, scalable serving infrastructure, and supporting multiple environments in increasingly common hybrid and multi-cloud setups. In this session, we will learn the concept of ""ML Ops"" (DevOps for ML) and how to leverage various Google initiatives like TFX, Kubeflow Fairing (Hybrid ML SDK) and Kubeflow Pipelines to build and maintain production quality ML systems. Access and Analyze Data → https://bit.ly/2UfqHpB Watch more: Next '19 ML & AI Sessions here → https://bit.ly/Next19MLandAI Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the G Suite Channel → https://bit.ly/G-Suite1 Speaker(s): Kaz Sato, Zia Syed, Robin Zondag, Fabien Da Silva Session ID: MLAI101 product:Cloud Storage,Kubernetes Engine,Virtual Private Cloud (VPC); fullname:Neeve Nikoo;
http://bit.ly/2VyXIcB G Suite April 10, 2019 at 09:50PM
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