AWS Generative AI For Executives
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Overview
In this course, you will learn how to leverage generative artificial intelligence (AI) within your organization. We’ll cover how to drive business value with generative AI, the use cases across various industries, and the considerations to implement generative AI safely and responsibly. The goal of this course is to provide you with the fundamental concepts and tools you’ll need to successfully lead generative AI initiatives within your organization.
Course Objective
In this course, you will learn to:
- Recognize the potential business value of generative AI
- Identify real world use cases that you can implement today
- Manage the people, process, and technology changes needed to be successful
- Use generative AI safely and responsibly
- Identify the specific steps you can take to get started with generative AI
Who Should Attend
This course is intended for:
- Executives and Senior Business Leaders
Prerequisites
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Introduction to Generative AI
- Definitions and terminology
- AWS approach to generative AI
Module 2 - Generative AI Use Cases
- Common use cases
- Real-world case studies
Module 3 - Overcoming Technical and Organizational Challenges
- Security
- Accuracy
- Cost
- People and culture
Module 4 - Implementation
- Identifying your use case
- Assessing data, technology, people, and processes
- Evaluating business impact and scaling
Module 5 - Next Steps in Your Generative AI Journey
- Next steps and additional resources
Course summary
FAQs
General Questions:
- Q: What is Generative AI?
- A: Generative AI is a branch of artificial intelligence focused on creating new data instances that resemble training data. It can generate various forms of content, including text, images, audio, and synthetic data.
- Q: How does Generative AI work?
- A: Generative AI models learn the underlying patterns and structure of input data and then use this knowledge to generate new data with similar characteristics. Techniques used include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.
- Q: What are some applications of Generative AI?
- A: Generative AI has a wide array of applications, such as:
- Creating realistic images and videos
- Generating human-like text for chatbots, articles, and scripts
- Composing music
- Designing new products
- Drug discovery
- A: Generative AI has a wide array of applications, such as:
- Q: What are the benefits of using Generative AI?
- A: Benefits include:
- Automation of content creation
- Increased creativity and innovation
- Personalized experiences
- Solving complex problems
- A: Benefits include:
- Q: What are the challenges and risks associated with Generative AI?
- A: Challenges and risks include:
- Potential for misuse (e.g., deepfakes)
- Bias in generated content
- Copyright infringement issues
- Ethical concerns about job displacement
- A: Challenges and risks include:
- Q: How is Generative AI different from traditional AI?
- A: Traditional AI often focuses on tasks like classification, prediction, and automation based on existing data. Generative AI goes a step further by creating new content.
- Q: How can businesses use Generative AI
- A: Businesses can use Generative AI for:
- Marketing and content creation
- Product development
- Customer service (e.g., chatbots)
- Data augmentation
- A: Businesses can use Generative AI for:
- Q: What are the ethical considerations surrounding Generative AI?
- A: Ethical considerations include:
- Addressing bias in AI models
- Ensuring responsible use of generated content
- Transparency about AI-generated content
- Protecting privacy and security
- A: Ethical considerations include:
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