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所在平台: Udemy |
课程主页: https://www.udemy.com/course/serverless-microservices-on-aws-in-10-hours/
课程评论：没有评论
课程名称：AWS无服务器微服务，10小时 课程概述： 微服务是一种流行的应用程序开发方法，旨在构建可维护、可扩展的云基础应用。在这门课程中，您将学习如何在AWS平台上构建微服务，与无服务器计算结合，为开发者提升生产力，实现自动扩展并降低运营成本。课程内容涵盖无服务器计算和微服务的基本概念，使组织能够依托云提供商管理服务器和容量规划，从而简化大规模部署和运行的过程。 该课程包括两个完整的部分，实用且全面。第一个部分是“构建可扩展的无服务器微服务REST数据API”，介绍了构建无服务器应用的实用解决方案，展示如何为组织开发端到端的无服务器应用。我们选择了一个数据API使用案例，帮助您在降低成本的同时提供更大的灵活性。第二个部分是“实施无服务器微服务架构模式”，展示如何使用无服务器计算在AWS上实现主要的微服务架构模式。课程将教您在持续集成和交付流水线中如何运用无服务器计算，从而提高开发团队的交付速度、生产力和灵活性。 学习成果： - 能够轻松构建、测试、部署、扩展和监控微服务及API，使用无服务器计算和持续交付流水线。 - 设计高可用且成本高效的微服务应用，管理基础设施、可扩展性和安全性。 专家介绍： 本课程由Richard T. Freeman博士授课，他是一位经验丰富的云架构师和机器学习顾问，在多个行业中有着广泛的工作背景和成功经验，尤其是在AWS无服务器计算方面，拥有超过四年的生产经验。 通过这门课程，您将掌握无服务器微服务的构建与实施，提升云应用的实现能力，适应不断变化的开发需求。
Microservices are a popular new approach to building maintainable, scalable, cloud-based applications. AWS is the perfect platform for hosting Microservices. Recently, there has been a growing interest in serverless computing due to the increase in developer productivity, built in auto-scaling abilities, and reduced operational costs.In combining both microservices and serverless computing, organizations will benefit from having the servers and capacity planning managed by the cloud provider, making them much easier to deploy and run at scale.This comprehensive 2-in-1 course is a step-by-step tutorial which is a perfect course to implementing microservices using serverless computing on AWS. Build highly available microservices to power applications of any size and scale. Get to grips with microservices and overcome the limitations and challenges experienced in traditional monolithic deployments. Design a highly available and cost-efficient microservices application using AWS. Create a system where the infrastructure, scalability, and security are managed by AWS. Finally, reduce your support, maintenance, and infrastructure costs.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Building a Scalable Serverless Microservice REST Data API, covers practical solutions to building Serverless applications. In this course we show you how to build an end-to-end serverless application for your organization. We have selected a data API use case that could reduce costs and give you more flexibility in how you and your clients consume or present your application, metrics and insight data. We make use of the latest serverless deployment and build framework, share our experience on testing, and provide best practices for running a serverless stack in a production environment. The second course, Implementing Serverless Microservices Architecture Patterns, covers implementing Microservices using Serverless Computing on AWS. In this course, We will show you how Serverless computing can be used to implement the majority of the Microservice architecture patterns and when put in a continuous integration & continuous delivery pipeline; can dramatically increase the delivery speed, productivity and flexibility of the development team in your organization, while reducing the overall running, operational and maintenance costs. By the end of the course, you'll be able to build, test, deploy, scale and monitor your microservices with ease using Serverless computing in a continuous delivery pipeline.By the end of this course, you will be able to build, test, deploy, scale, and monitor your APIs and microservices with ease using serverless computing in a continuous delivery pipeline. Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Richard T. Freeman, PhD currently works for JustGiving, a tech-for-good social platform for online giving that's helped 25 million users in 164 countries raise $5 billion for good causes. He is also offering independent and short-term freelance cloud architecture & machine learning consultancy services. Richard is a hands-on certified AWS Solutions Architect, Data & Machine Learning Engineer with proven success in delivering cloud-based big data analytics, data science, high-volume, and scalable solutions. At Capgemini, he worked on large and complex projects for Fortune Global 500 companies and has experience in extremely diverse, challenging and multi-cultural business environments. Richard has a solid background in computer science and holds a Master of Engineering (MEng) in computer systems engineering and a Doctorate (Ph.D.) in machine learning, artificial intelligence and natural language processing. See his website for his latest blog posts and speaking engagements. He has worked in nonprofit, insurance, retail banking, recruitment, financial services, financial regulators, central government and e-commerce sectors, where he:-Provided the delivery, architecture and technical consulting on client site for complex event processing, business intelligence, enterprise content management, and business process management solutions.-Delivered in-house production cloud-based big data solutions for large-scale graph, machine learning, natural language processing, serverless, cloud data warehousing, ETL data pipeline, recommendation engines, and real-time streaming analytics systems.-Worked closely with IBM and AWS and presented at industry events and summits, published research articles in numerous journals, presented at conferences and acted as a peer-reviewer.-Has over four years of production experience with Serverless computing on AWS.
