AWS offers new artificial intelligence, machine learning, and generative AI guides to plan your AI strategy

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Breakthroughs in artificial intelligence (AI) and machine learning (ML) have been in the headlines for months—and for good reason. The emerging and evolving capabilities of this technology promises new business opportunities for customer across all sectors and industries. But the speed of this revolution has made it harder for organizations and consumers to assess what these breakthroughs mean for them specifically.

Over the years, AWS has invested in the democratizing of access to—and understanding of —AI, ML and generative AI. Through announcements around the latest developments in generative AI and the establishment of a $100 million Generative AI Innovation Center program, Amazon Web Services (AWS) has been at the forefront of helping drive understanding about the role that these innovations can play in the lives of both individuals and organizations. To help you understand your options in relation to AI and ML, AWS has published two new guides: the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI and the Getting Started Resource Center machine learning decision guide.

AWS CAF for AI, ML, and Generative AI

The AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI (CAF-AI) is designed to help you navigate your AI journey. It’s a mental model for organizations that strive to generate business value from AI/ML. Based on our own—and our customers’—experience, we provide in this framework of best practices for an AI transformation and accelerate business outcomes through innovative use of AI on AWS.

Used by customers and partner teams, CAF-AI helps derive, prioritize, evolve, and communicate a strategy for AI transformation. The following figure shows how we simplify an AI journey through CAF-AI: by working backward from business outcomes (1) to the opportunities that AI, ML, and generative AI provide (2), across your transformation domains (3) and your foundational capabilities (4) through an iterative process (5) of assessing, deriving, and implementing action items for an AI strategy.

In CAF-AI, we describe the AI/ML journey you may experience as your organizational capabilities on AI and ML mature. To guide you, we zoom in on the evolution of foundational capabilities that we have observed assist an organization to grow its maturity in AI further.

We also provide prescriptive guidance through an overview of the target state of these foundational capabilities and explain how to evolve them step by step to generate business value along the way. The following figure shows these foundational capabilities for cloud and AI/ML adoption. A capability is an organizational ability to use processes to deploy resources (such as people, technology, and other tangible or intangible assets) to achieve an outcome. Because the CAF-AI is a living index of knowledge, you can expect it to grow and change over time.

Designed as a starting and orientation point throughout a customer’s ML and AI journey, CAF-AI is intended to be a document that organizations can draw inspiration from as they shape their mid-term AI and ML agenda and try to understand the important topics and perspectives that influence it. Depending on where you are at on your AI/ML journey, you might focus on a specific section and hone your skills there, or use the whole document to judge maturity and help direct near-term improvement areas.

Because the business problem space to which AI/ML can be applied isn’t a single function or domain, it applies across all functions of businesses and all industry domains where you are looking for ways to reset the playing field in markets where AI/ML does make an economical difference. The AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI is one of the many tools AWS provides to help you achieve this outcome. As AI/ML enables solutions and solution paths to problems that have remained uneconomical to solve for decades (or were technically impossible to tackle without AI/ML), the resulting business outcomes can be profound.

The Getting Started Resource Center machine learning decision guide

AWS has always been about choice. As you ramp up your use of AI, it is paramount that you have the right support in choosing the best service, model, and infrastructure for your business needs. The Getting Started Resource Center machine learning decision guide is designed to provide you with a detailed overview of the AI and ML services offered by AWS, and provide structured guidance on how to choose the services that might be right for you and your use cases.

The decision guide can also help you articulate and consider the criteria that will inform your choices. For example, it describes the range of AWS ML services (see the following screenshot), each of which caters to different levels of management requirement, depending on how much control and customization you need.

The guide also explains the unique capabilities of AWS services in realizing the power of foundation models and where you can make the most of this fast-evolving branch of machine learning.

It offers details on specific services, links to detailed, service-level technical guides, a comparison table that highlights the unique capabilities of key services, and criteria for selecting AI and ML services. It also provides a curated set of links to key resources that can help you get started in using AI, ML, and generative AI services on AWS.

If you want to understand the breadth of AI, ML, and generative AI offerings provided by AWS, this decision guide is a great place to start.

Conclusion

The Getting Started Resource Center machine learning decision guide, together with the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI, covers the technical and non-technical questions that we often hear. We hope you find these new resources useful and look forward to your feedback on them.

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About the Authors

Caleb Wilkinson has more than a decade of experience building AI solutions. As a Senior Machine Learning Strategist at AWS, Caleb pioneers innovative applications of AI that push the boundaries of possibility and helps organizations benefit responsibly from artificial intelligence. He is the co-author of CAF-AI.

Alexander Wöhlke has a decade of experience in AI and ML. He is Senior Machine Learning Strategist and Technical Product Manager at the AWS Generative AI Innovation Center. He works with large organizations on their AI-Strategy and helps them take calculated risks at the forefront of technological development. He is the co-author of CAF-AI.

Geof Wheelwright manages the AWS decision content team, which writes and develops the growing collection of decision guides on the AWS Getting Started Resource Center. His team created the Choosing an AWS machine learning decision guide. He has enjoyed working with AI and its ancestors since first being introduced to simple, text-based Apple II versions of ELIZA in the early 1980s.

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