Generative AI with LLMs

up to 4 weeks
Intermediate

In Generative AI with Large Language Models (LLMs), created in partnership with AWS, you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications. Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works.

Generative AI
Large Language Models
Transformer Architecture
Model Training
Fine-Tuning

Overview

This course provides a comprehensive understanding of generative AI, covering key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection to performance evaluation and deployment. You will learn about the transformer architecture, empirical scaling laws, and state-of-the-art training, tuning, inference, tools, and deployment methods. The course also discusses the challenges and opportunities that generative AI creates for businesses, with insights from industry researchers and practitioners.

Online
course location
English
course language
Professional Certification
upon course completion
Self-paced
course format
Live classes
delivered online

Who is this course for?

Data Scientists

Gain deeper knowledge into the underlying structure and mechanisms of generative AI and explore avenues for further innovations in this field.

Machine Learning Engineers

Learn how to better train, optimize and fine-tune generative models while learning about different use cases and applications.

Prompt Engineers

Explore advanced prompting techniques and learn how to control your output using generative configuration parameters.

Research Engineers

Explore the state of art generative models and architectures in depth to build on top of with your own advanced techniques in generative AI.

Anyone interested in generative AI

Get an extensive introduction to developing with generative AI and its fundamentals.

Why should you take this course?

Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works. Learn from expert AWS AI practitioners and apply state-of-the-art methods to maximize model performance. Ideal for data scientists, machine learning engineers, prompt engineers, research engineers, and anyone interested in generative AI.

Pre-Requisites

1 / 2

Experience coding in Python
Familiarity with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets

What will you learn?

Introduction to Generative AI

Learn the basics of generative AI and its applications in various industries.

Understanding Large Language Models

Dive into the architecture and training of large language models, including transformers.

Empirical Scaling Laws

Learn how to optimize model performance based on dataset size, compute budget, and inference requirements.

Model Training and Fine-Tuning

Explore state-of-the-art training and fine-tuning methods to adapt LLMs to specific use cases.

Advanced Prompting Techniques

Learn advanced techniques for controlling output using generative configuration parameters.

AI Deployment

Understand the methods and tools for deploying generative AI models in real-world applications.

Challenges and Opportunities in Generative AI

Discuss the challenges and opportunities that generative AI creates for businesses, with insights from industry researchers and practitioners.

What learners say about this course

“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
Jan Zawadzki
Data Scientist at Carmeq
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
Kritika Jalan
Data Scientist at Corecompete Pvt. Ltd.
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
Chris Morrow
Sr. Product Manager at Amazon

Meet your instructors

Antje Barth
Principal Developer Advocate, Generative AI, Amazon Web Services (AWS)
Antje Barth is a Principal Developer Advocate for generative AI at AWS, with over 17 years of experience in the IT industry. She co-authored the O'Reilly books "Generative AI on AWS" and "Data Science on AWS" and developed the "Generative AI with large language models" and "Practical Data Science" courses with DeepLearning.AI.
Chris Fregly
Principal Engineer, Generative AI, Amazon Web Services (AWS)
Chris Fregly is a Principal Engineer at Amazon Web Services (AWS) with expertise in generative AI. He has a wealth of experience in the computer software industry, having previously held leadership roles at Netflix and Databricks.
Shelbee Eigenbrode
Principal AI/ML Specialist Solutions Architect, Amazon Web Services (AWS)
Shelbee Eigenbrode is a Principal AI/ML Specialist Solutions Architect at Amazon Web Services (AWS). She is passionate about helping customers utilize AI/ML to drive business outcomes.
Mike Chambers
Specialist Developer Advocate, Machine Learning, Amazon Web Services (AWS)
Mike Chambers is an AI Specialist Developer Advocate at Amazon Web Services (AWS), where he helps developers create innovative and impactful applications using generative AI and cloud computing. He is also an ex AWS Machine Learning Hero, a recognition for his outstanding knowledge-sharing and community-building efforts in the AWS ML ecosystem.

Upcoming cohorts

Cost
€10
Duration
4 weeks
Dates
start now
Location
Online

€10