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Learn how to deploy, manage, and scale containerized applications using Kubernetes on AWS
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Kubernetes on AWS: Step-By-Step Tutorial for Deployment
Kubernetes is a one of the top cloud platform technology and offers some of the best solution over other cloud platforms. Major cloud infrastructure providers, such as AWS, Azure, and Google, offer hosted versions of Kubernetes. If you’re a software architect or a software engineer who wants to build and manage container-based applications on AWS with the powerful orchestration tool Kubernetes, then go for this Learning Path. This comprehensive 2-in-1 course is a perfect mix of concepts and practice that will help you to build, manage, and deploy container-based applications on AWS using Kubernetes. You will you learn how to use Kubernetes pods, services, replication controllers, and labels to manage your clusters effectively and will get a feel for how to handle networking with Kubernetes. You will also learn how to install and deploy Kubernetes on several cloud platforms. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Learning Kubernetes, teaches you how to extend the opportunities that containerization innovations have brought about in a new and even more effective way. You will then get started with the basics, explore the fundamental elements of Kubernetes, and find out how to install it on your system, before digging a little deeper into Kubernetes core constructs. Finally, you will learn how to use Kubernetes pods, services, replication controllers, and labels to manage your clusters effectively and also get a feel for how to handle networking with Kubernetes. In the second course, Hands-on Kubernetes on AWS, you will jump into Kubernetes architecture, and grasp what the main components and services are, and how they come together to build a production-class container infrastructure. Next, you will learn how to install and deploy Kubernetes on several cloud platforms. Finally, you will explore more advanced topics on Kubernetes, including continuous integration, high availability, and disaster recovery using Kubernetes. By the end of this Learning Path, youll have gained plenty of hands-on experience with Kubernetes on Amazon Web Services. Youll also have picked up some tips on deploying and managing applications, keeping your cluster and applications secure, and ensuring that your whole system is reliable and resilient to failure. Meet Your Expert(s): We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: Braithe E.S. Warnock is currently a managing cloud architect for the financial services division of Ernst & Young. He had the opportunity to work with several of the largest PCF installations on an international scale. He helped build the framework for the adoption of PCF at top companies such as Ford, Comcast, DISH, HSBC, and Charles Schwab. As a vendor-neutral consultant, he enjoys helping people understand the rapidly-evolving world of cloud and application architecture. He has more than six years of experience and specialization in global digital transformations. He has expertise in various cloud and cloud platform technologies (PCF, AWS, Azure, VMware, Netflix OSS, Kubernetes, and OpenShift) and also the Java and Spring Boot frameworks. He has developed over 100 microservices using Spring Boot, Java 7/8, Spring Cloud, and Netflix OSS, spanning half a dozen unique cloud-native microservice architectures. Alan Rodrigues has been working on software components such as Docker containers and Kubernetes for the last 2 years. He has extensive experience working on the AWS platform, currently being certified as an AWS Solution Architect Associate, a SysOps Administrator and a Developer Associate. He has seen organizations moving towards using containers as part of their microservices architecture. And there is a strong need to have a container orchestration tool in place. Kubernetes is by far the most popular container orchestration being adopted. He also has experience in developing machine learning models using AWS, Spark, and MLlib to support product recommendations and enhance customer data.
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