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.
Wer teilnehmen sollte
This course is intended for the following:
- Individuals interested in machine learning and artificial intelligence, independent of a specific job role
Kursvoraussetzungen
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
Was du lernen wirst
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.
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