Developer Training

This hands-on, advanced technical-level, instructor-led course provides a deep dive into Amazon Web Services (AWS) best practices to help you perform effective and efficient AWS Well-Architected Framework reviews. The course covers the phases of a review, including how to prepare, run, and get guidance after a review has been performed.

The Advanced Developing on AWS course uses the real-world scenario of taking a legacy, on-premises monolithic application and refactoring it into a serverless microservices architecture. This three-day advanced course covers advanced development topics such as architecting for a cloud-native environment; deconstructing on-premises, legacy applications and repackaging them into cloud-based, cloud native architectures; and applying the tenets of the Twelve-Factor Application methodology.

AWS Technical Essentials introduces you to AWS services, and common solutions. It provides you with fundamental knowledge to become more proficient in identifying AWS services. It helps you make informed decisions about IT solutions based on your business requirements and get started working on AWS.

In this course, you’ll learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You’ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You’ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS.

Learn how to use the AWS Software Development Kit to develop secure and scalable cloud applications. You will be able to setup AWS SDK and developer
credentials for Java, C#/.NET, Python, and JavaScript. Explore how to interact with AWS services, and develop solutions by using the AWS SDK. Also learn about key concepts, best practices, and troubleshooting tips.

Running Container-Enabled Microservices on AWS is designed to teach you how to manage and scale container-enabled applications by using Amazon Elastic Container Service (ECS). This course highlights the challenges of running containerized applications at scale and provides guidance on creating and using Amazon ECS to develop and deploy containerized microservices-based applications. In the hands-on lab exercises you will use Amazon ECS to handle long-running services, build and deploy container images, link services together, and scale capacity to meet demand. You will also learn how to run container workers for asynchronous application processes.

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.