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.

Building a microservices platform using virtual machines or containers, involves a lot of initial and ongoing effort. There is a cost associated with having idle services running, maintenance of the boxes and a configuration complexity involved in scaling up and down.

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 3-in-1 course is a step-by-step tutorial which is a perfect course to implementing Microservices using Serverless Computing on AWS. Build highly availableMicroservices 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.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Building Microservices on AWS, covers building highly available Microservices to power applications of any size and scale. This course shows you how to build Microservices-based applications on AWS. Overcome the limitations and challenges you experience in traditional monolith deployments. By the end of the course, you'll have learned to apply AWS tools to create and deploy Microservices-based applications. You'll be able to make your applications cost-effective, easier to scale, and faster to develop.

The second course, Building a Scalable ServerlessMicroservice 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 third course, Implementing ServerlessMicroservices 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 the course, you’ll create a secure, scalable, and Serverless data API to build highly available Microservices to power applications of any size and scale.

About the Authors

●        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 that organizations are 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 on the market.

●        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.