Introduction

Generative AI has rapidly emerged as a transformative force, revolutionizing industries from content creation to drug discovery. At the heart of this revolution lie Large Language Models (LLMs), which have the potential to revolutionize how we interact with information and generate new content.

This course serves as a foundational introduction to the generative AI lifecycle, providing you with a comprehensive overview of the key stages involved in developing and deploying LLMs. By understanding the entire process, you’ll gain valuable insights into the challenges, opportunities, and best practices associated with generative AI.

Course Objectives

• Gain a foundational understanding of the key stages in the generative AI lifecycle.

• Explore the role of LLMs in driving innovation and problem-solving.

• Learn about the importance of data quality and preprocessing in LLM development.

• Understand the different techniques used to train and fine-tune LLMs.

• Explore the role of evaluation metrics in assessing LLM performance.

• Discover the potential applications of LLMs across various domains.

Course Structure

This course is designed to provide a concise overview of the generative AI lifecycle. Each lecture will introduce a key stage, providing you with essential information and context. For a more in-depth exploration of each topic, we recommend our comprehensive course, “Mastering Generative AI: From LLMs to Applications.”

Key Topics Covered

• Introduction to Generative AI and LLMs

• The Generative AI Lifecycle

• Model Selection for Pre-trained models

• Model Training and Fine-Tuning

• Evaluation Metrics and Benchmarking

• Applications of Generative AI

By completing this course, you’ll have a solid foundation in the generative AI lifecycle, enabling you to make informed decisions and effectively leverage LLMs in your work. We encourage you to explore our more advanced course, “Mastering Generative AI: From LLMs to Applications,” for a deeper dive into each topic and practical hands-on experience.

Who this course is for:

• Tech managers
• directors
• ML Engineers
• other tech leaders
• Software Engineers
• AI Developers
• Data Scientists