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, how LLMs are trained, and how fine-tuning enables LLMs to be adapted to various specific use cases. The course also covers empirical scaling laws, state-of-the-art training, tuning, inference tools, and deployment methods to maximize model performance. Additionally, you will discuss the challenges and opportunities that generative AI creates for businesses.

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?

This course offers key benefits such as foundational knowledge, practical skills, and a functional understanding of generative AI. It covers main topics like transformer architecture, model training, fine-tuning, and deployment. Ideal for data scientists, machine learning engineers, and anyone interested in generative AI, this course will help you advance your career and achieve your goals.

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 structure and mechanisms of large language models (LLMs).

Transformer Architecture

Explore the transformer architecture that powers LLMs.

Model Training and Fine-Tuning

Learn how LLMs are trained and how fine-tuning enables adaptation to specific use cases.

Empirical Scaling Laws

Use empirical scaling laws to optimize the model’s objective function.

Advanced Prompting Techniques

Explore advanced prompting techniques and generative configuration parameters.

Model Deployment

Apply state-of-the-art deployment methods to maximize model performance.

Performance Evaluation

Evaluate the performance of generative AI models.

Challenges and Opportunities

Discuss the challenges and opportunities that generative AI creates for businesses.

Case Studies and Industry Applications

Hear stories from industry researchers and practitioners about real-world applications of generative AI.

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
Antje Barth is a Principal Developer Advocate for generative AI at Amazon Web Services, with over 17 years of experience in the IT industry. She is an author, instructor, and co-founder of the Women in Big Data chapter in Duesseldorf, Germany.
Chris Fregly
Principal Engineer, Generative AI, Amazon Web Services (AWS)
Chris Fregly is an experienced Founder with a demonstrated history of working in the computer software industry. He is a strong entrepreneurship professional skilled in Apache Spark, TensorFlow, Scikit-Learn, R, PMML, PFA, ONNX, Xgboost, and more.
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 is a Specialist Developer Advocate for Machine Learning at Amazon Web Services, where he helps developers create innovative AI solutions using cloud computing. He is an ex AWS Machine Learning Hero, recognized for his knowledge-sharing and community-building efforts.

Upcoming cohorts

Cost
€15
Duration
4 weeks
Dates
start now
Location
Online

€15