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This course is for individuals who seek an overall understanding of the Amazon Web Services (AWS) Cloud, independent of specific technical roles.

Prepping data sets for machine learning and thinking about results

Learn how to run Kubernetes on AWS without needing to maintain your own Kubernetes control plane.

Increase productivity and agile application development by learning how to build, secure, deploy, and manage serverless applications using AWS Lambda.

AWS data lake, which is a single platform combining storage, data governance, and analytics, is designed to address the challenges.

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use...

Designed for novice and experienced networking engineers, this course covers essential topics, best practices, and hands-on labs. Its purpose is to equip learners with the knowledge and skills that are required to design, configure, and optimize network infrastructur...

Gain a solid understanding of use cases where generative AI can provide solutions and address business needs. Additionally, you will learn about practical insights into technologies related to generative AI and how you can use those technologies to solve real-world p...

Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS.

Through synchronous, on-site instructor-led training in Nashville, Tennessee and Pittsburgh, Pennsylvania Global Knowledge was able to help the organization create standardized language and process around the cloud through customized foundation classes