Running Container-Enabled Microservices on AWS is designed to teach you how to manage and scale container-enabled applications by using Amazon EC2 Container Service (ECS).
In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.
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.
Deep Learning on AWS is a one-day course that introduces you to cloud-based Deep Learning (DL) solutions on Amazon Web Services (AWS)
In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon Quicksight, Amazon Athena and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.
Learn how to create automatable and repeatable deployments of networks and systems on the AWS platform.
Learn how to build complex AWS solutions incorporating data services, governance, and security.
Security Engineering on AWS demonstrates how to efficiently use AWS security services to stay secure and compliant in the AWS Cloud.
*Class taught in Mandarin* The training will enable you to make informed decisions about IT solutions based on your business requirements and get started working on the AWS solutions and services.