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Development of AI Solutions Generative

Category

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
Assessing the performance of an FM
Deployment and its impact on business objectives
This course is an introduction to generative AI courses, which delve into concepts related to customizing an FM using on-the-fly engineering, recovery augmented generation (RAG), and fine-tuning.

Course level: Fundamental
Duration: 1 hour

Activities

This course includes interactive elements, videos, text instructions, 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 tradeoffs of various approaches to foundation model customization.
Understand the role of agents in multi-step tasks.
Understand approaches to evaluating foundation model performance.
Identify relevant metrics to evaluate base model performance.

This course is aimed at the following:

Individuals interested in machine learning and artificial intelligence, regardless of a specific role

prerequisites

The development of generative AI solutions is part of a series that facilitates a foundation in artificial intelligence, machine learning, and generative AI. If you have not already done so, it is recommended that you complete these two courses:

Fundamentals of Machine Learning and Artificial Intelligence
Exploring artificial intelligence use cases and applications

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