Developing Generative Artificial Intelligence Solutions

Resumen

In this course, you will explore the generative artificial intelligence (generative AI) application lifecycle, which includes the following:

• Defining a business use case
• Selecting a foundation model (FM)
• Improving the performance of an FM
• Evaluating the performance of an FM
• Deployment and its impact on business objectives

This course is a primer to generative AI courses, which dive deeper into concepts related to customizing an FM using prompt engineering, Retrieval Augmented Generation (RAG), and fine-tuning.

• Course level:

  Fundamental

• Duration:

  1 hour

Activities

This course includes interactive elements, videos, text instruction, and illustrative graphics.

Course objectives

In this course, you will learn how to do the following:

• Identify selection criteria to choose pre-trained models.
• Define Retrieval Augmented Generation (RAG) and describe its business application.
• Explain the cost trade-offs of various approaches to foundation model customization.
• Understand the role of agents in multi-step tasks.
• Understand approaches to evaluate foundation model performance.
• Identify relevant metrics to assess foundation model performance.

Intended audience

This course is intended for the following:

• Individuals interested in machine learning and artificial intelligence, independent of a specific job role

Prerequisites

Developing Generative AI Solutions is part of a series that facilitates a foundation on artificial intelligence, machine learning, and generative AI. If you have not done so already, it is recommended that you complete these two courses:

• Fundamentals of Machine Learning and Artificial Intelligence
• Exploring Artificial Intelligence Use Cases and Applications

Course outline

Section 1

• Lesson 1:

  How to Use This Course

Section 2:

Introduction

• Lesson 2:

  Course Overview

• Lesson 3:

  Generative AI Application Lifecycle

Section 3:

Defining the Use Case

• Lesson 4:

  Defining a Use Case

Section 4:

Selecting a Foundation Model

• Lesson 5:

  Selecting an FM

• Lesson 6:

  Knowledge Check

Section 5:

Improving Performance

• Lesson 7:

  Improving the Performance of an FM

• Lesson 8:

  Knowledge Check

Section 6:

Evaluating Results

• Lesson 9:

  Evaluating an FM

• Lesson 10:

  Knowledge Check

Section 7:

Deployment

• Lesson 11:

  Deploying the Application

Section 8:

Conclusion

• Lesson 12:

  Course Summary

• Lesson 13:

  Resources

• Lesson 14:

  Contact Us

University:

AWS Skill Builder

Materias