AWS Ramp-Up Guide: Academic Research
AWS Ramp-Up Guides offer a variety of resources to help you build your skills and knowledge of the AWS Cloud. Each guide features carefully selected digital training, classroom courses, videos, whitepapers, certifications, and more. AWS now offers four ramp-up guides that help academic researchers who use AI, ML, Generative AI, and HPC in their research activities, as well as the essential AWS knowledge for Statistician Researchers and Research IT professionals. The guides help learners decide where to start, and how to navigate, their learning journey. Some resources will be more relevant than others based on each learner’s specific research tasks.
AI, ML, Generative AI ramp-up guide (page 2) is for academic researchers who are exploring using AWS AI, ML, and Generative AI tools to improve efficiency and productivity in their research tasks. This course introduces seven components on AI and ML and ten components on Generative AI. The course starts with an introduction to AI, and covers AWS AI/ML services, such as Amazon SageMaker. The Generative AI content covers topics such as planning a Generative AI project, responsible AI Practices, security, compliance, and governance for AI solutions. The Generative AI topics also cover how to get started with Amazon Bedrock. Recommended prerequisites: basic understanding of Python.
High Performance Computing ramp-up guide (page 3) is designed for academic researchers who seek to use HPC on AWS. In this course, you will be introduced to eleven components that are essential about Higher Performance Computing on AWS. The course starts with an overview of HPC on AWS, followed by topics including AWS ParallelCluster and Research HPC Workloads on AWS Batch. Recommended prerequisites: complete AWS Cloud Essentials.
Statistician Researcher ramp-up guide (page 4) is specifically catered for researchers in the fields of statistics and quantum analysis. The course covers topics such as building with Amazon Redshift clusters, getting started with Amazon EMR, Machine Learning for Data Scientists, authoring visual analytics using Amazon QuickSight, Batch analytics on AWS, and Amazon Lightsail for Research. Recommended prerequisites: complete AWS Cloud Essentials.
Research IT ramp-up guide (page 5) is an extension of the Foundational Researcher Learning Plan, and enables Research IT leaders and professionals to dive deeper into specific topics. The goal of this extension for Research IT professionals is to dive deeper on fundamentals, understand management capabilities and implementing guardrails, cost optimization for research workloads, become familiar with platforms for research and research partners, and learn more about AWS Landing Zone and AWS Control Tower for Research. Recommended prerequisites: Foundational Researcher Learning Plan.
Licence: Other (Open)
Contact: emmarrig@amazon.com
Keywords: Machine learning, machine learning, aws, AWS, cloud, Cloud computing, cloud computing, training material, HPC training, HPC, training registry, training partnerships
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AWS Ramp-Up Guide: Academic Research
https://d1.awsstatic.com/training-and-certification/ramp-up_guides/Ramp-Up_Guide_Academic_Research.pdf
https://dresa.org.au/materials/aws-ramp-up-guide-academic-research
AWS Ramp-Up Guides offer a variety of resources to help you build your skills and knowledge of the AWS Cloud. Each guide features carefully selected digital training, classroom courses, videos, whitepapers, certifications, and more. AWS now offers four ramp-up guides that help academic researchers who use AI, ML, Generative AI, and HPC in their research activities, as well as the essential AWS knowledge for Statistician Researchers and Research IT professionals. The guides help learners decide where to start, and how to navigate, their learning journey. Some resources will be more relevant than others based on each learner’s specific research tasks.
AI, ML, Generative AI ramp-up guide (page 2) is for academic researchers who are exploring using AWS AI, ML, and Generative AI tools to improve efficiency and productivity in their research tasks. This course introduces seven components on AI and ML and ten components on Generative AI. The course starts with an introduction to AI, and covers AWS AI/ML services, such as Amazon SageMaker. The Generative AI content covers topics such as planning a Generative AI project, responsible AI Practices, security, compliance, and governance for AI solutions. The Generative AI topics also cover how to get started with Amazon Bedrock. Recommended prerequisites: basic understanding of Python.
High Performance Computing ramp-up guide (page 3) is designed for academic researchers who seek to use HPC on AWS. In this course, you will be introduced to eleven components that are essential about Higher Performance Computing on AWS. The course starts with an overview of HPC on AWS, followed by topics including AWS ParallelCluster and Research HPC Workloads on AWS Batch. Recommended prerequisites: complete AWS Cloud Essentials.
Statistician Researcher ramp-up guide (page 4) is specifically catered for researchers in the fields of statistics and quantum analysis. The course covers topics such as building with Amazon Redshift clusters, getting started with Amazon EMR, Machine Learning for Data Scientists, authoring visual analytics using Amazon QuickSight, Batch analytics on AWS, and Amazon Lightsail for Research. Recommended prerequisites: complete AWS Cloud Essentials.
Research IT ramp-up guide (page 5) is an extension of the Foundational Researcher Learning Plan, and enables Research IT leaders and professionals to dive deeper into specific topics. The goal of this extension for Research IT professionals is to dive deeper on fundamentals, understand management capabilities and implementing guardrails, cost optimization for research workloads, become familiar with platforms for research and research partners, and learn more about AWS Landing Zone and AWS Control Tower for Research. Recommended prerequisites: Foundational Researcher Learning Plan.
emmarrig@amazon.com
Machine learning, machine learning, aws, AWS, cloud, Cloud computing, cloud computing, training material, HPC training, HPC, training registry, training partnerships