Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes GSP777
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Course Description
Creating google kubernetes engine course,
in this course we will learn about Creating Google Kubernetes Engine (GKE) Deployments, focusing on how to effectively deploy and manage containerized applications using GKE, a powerful, fully managed Kubernetes service on Google Cloud. Kubernetes is an open-source platform for automating deployment, scaling, and management of containerized applications, and GKE simplifies this process by providing a managed environment.
The course covers key topics such as setting up a Kubernetes cluster on GKE, configuring deployments, and defining the necessary resources, including CPU and memory, to ensure optimal performance. You will learn how to create and manage deployments that automatically handle scaling, rolling updates, and failover, ensuring that your applications are resilient and can handle varying levels of demand.
Additionally, the course will explore best practices for managing the lifecycle of your applications, including monitoring, logging, and troubleshooting within GKE. You’ll also learn how to integrate GKE with other Google Cloud services to build comprehensive, scalable cloud solutions.
By the end of this course, you will have the skills to create and manage robust GKE deployments, allowing you to leverage the power of Kubernetes to deploy, scale, and maintain your applications efficiently in the cloud. Whether you're new to Kubernetes or looking to refine your skills, this course will provide the knowledge needed to succeed with GKE.
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