Introduction to Generative AI for Business Leaders

Course Description

This course provides a strategic overview of Generative AI, tailored specifically for business leaders and decision-makers. It demystifies the technology, explaining what Generative AI is, how it evolved from traditional Machine Learning (ML), and its significance in today's business landscape. You will explore real-world use cases, understand the core concepts behind Foundation Models (FMs), and learn about the Generative AI services offered by AWS. Crucially, the course focuses on the practical aspects of implementation, covering the key steps in planning a successful Generative AI project and navigating the associated risks and benefits to drive real business value.

Who This Course Is For (Audience)

This course is designed for non-technical business leaders and decision-makers who are or will be involved in steering Generative AI initiatives. It is ideal for:

• C-Level Executives (CEO, CTO, CIO): Who need to understand the strategic implications of Generative AI.

• VPs and Directors: In areas like Strategy, Product, and Operations.

• Product and Project Managers: Responsible for overseeing the development and implementation of AI-powered features and products.

• Business Analysts: Who identify opportunities where Generative AI can solve business problems.

Course Objectives

Upon successful completion of this course, you will be able to:

• Define Generative AI: Clearly describe what Generative AI is and how it differs from traditional Machine Learning.

• Understand the Ecosystem: Explain the Generative AI services available at AWS and the history of ML that led to their development.

• Articulate Business Value: Discuss the strategic importance of Generative AI, including its key benefits and potential risks.

• Grasp Core Concepts: Understand the fundamentals of Foundation Models (FMs) and how they are trained at a high level.

• Plan Strategically: Outline the essential steps for planning a Generative AI project, from problem identification to risk mitigation.

Prerequisites

There are no technical prerequisites for this course. Participants should have a general understanding of business operations and an interest in technology's role in strategy. No programming or data science experience is required.

Curriculum

Section 1: The Foundation: Machine Learning (ML)

1. Overview of Machine Learning (ML)

2. A Brief History of ML

3. The Evolution from ML to the Emergence of Generative AI

Section 2: What is Generative AI?

1. Generative AI vs. Traditional Machine Learning

2. Common Generative AI Use Cases

3. Overview of Generative AI Services on AWS

Section 3: The Importance of Generative AI

1. Scenario: Applying Generative AI to a Business Challenge

2. Key Benefits: Innovation, Efficiency, and Personalization

3. High-Level Risks: Accuracy, Bias, and Security

Section 4: Generative AI Fundamentals

1. What are Foundation Models (FMs)?

2. How Foundation Models are Created (Pre-training)

3. How Unlabeled Data Gets Processed at a High Level

Section 5: How Generative AI "Thinks"

1. Tokenization

2. Word Embedding

3. The Decoder

4. The Final Output

Section 6: Providing Context to Generative AI

1. Prompting

2. Retrieval-Augmented Generation (RAG)

Section 7: Steps in Planning a Generative AI Project

1. Identify the Right Business Problem

2. Assess Data Readiness and Strategy

3. Choose the Right Model and Approach

4. Develop a Proof of Concept (PoC)

5. Plan for Scaling and Integration

Section 8: Risks and Mitigation

1. Accuracy and Hallucinations

2. Data Privacy and Security

3. Bias and Fairness

4. Cost Management

Testimonials