From Recipe to Chef - Become an LLM Engineer

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

8 hours 30 minutes
Sessions

On-Demand
Subtitles

English

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on an exciting culinary adventure of Large Language Models (LLMs), from the foundational ingredients to the final deployment of your own LLM-powered app. Through each module, you'll gain hands-on experience in model training, fine-tuning, and deployment, equipping you with the skills to become a proficient LLM engineer. By the end, you’ll understand how LLMs are created, optimized, and evaluated, and how they’re applied to real-world problems. Your learning journey will start with understanding the core principles behind LLMs, like data tokenization, training mechanisms, and the nuances of prompt engineering. As you dive deeper, you'll explore different architectures and learn how to fine-tune LLMs to suit specific needs, using techniques like transfer learning and low-rank adaptation. From there, you’ll get hands-on with deploying LLMs into production environments and building interactive applications using tools like Gradio, Streamlit, and LangChain. Whether you’re new to AI or looking to refine your skills, this course will walk you through the process of designing and developing LLM-powered solutions. By the end, you’ll not only have built a fully functional LLM app, but you will also be ready to enter the booming field of LLM engineering with the skills and confidence to make an impact. By the end of the course, you will be able to understand the fundamentals of LLMs, create and fine-tune your own models, evaluate their effectiveness, deploy them in real-world applications, and monitor and improve their performance over time. Additionally, you’ll have developed a strong portfolio of LLM projects to showcase your expertise.

Syllabus

What's Cooking? Intro to LLMs

In this module, we will introduce you to the fascinating world of LLMs, tracing their evolution from rudimentary algorithms to advanced, sophisticated systems. You’ll also discover how LLMs function differently from traditional AI, and get a taste of the most popular models in use today. By the end, you'll be prepared to dive deeper into the mechanics of LLMs.

Ingredients Matter – Understanding Data

In this module, we will explore how essential data is as the key ingredient in training LLMs. You’ll learn about tokenization, diverse datasets, and how high-quality data ensures a more accurate and effective model. Additionally, we’ll discuss the impact of biases and how they can be managed during the training process.

Cooking at Scale – Model Training Basics

In this module, we will break down the step-by-step process of training LLMs at scale, from mixing data to fine-tuning the model. You’ll get an inside look at the hardware required to train these models efficiently and explore different training methods like pretraining and fine-tuning for optimal performance.

Prompt Engineering – Seasoning for the Perfect Output

In this module, we will dive into the art of prompt engineering—how the right combination of instructions can lead to better responses from your LLM. You’ll learn about different prompting styles, how to craft the best prompts for specific tasks, and how to evaluate their effectiveness.

Fine-Tuning – Customizing the Recipe

In this module, we will explore how fine-tuning transforms a general LLM into a specialized tool for specific applications. You’ll learn about transfer learning, techniques for efficient fine-tuning, and hands-on methods to apply fine-tuning on your own datasets.

Evaluating LLMs – Taste Testing

In this module, we will focus on how to evaluate the quality of LLM outputs through both data-driven metrics and human feedback. We’ll also discuss how to detect and correct common model errors, like hallucinations, and how to ensure fairness by addressing bias in LLMs.

Serving Your Dish – Deploying LLMs

In this module, we will guide you through the deployment phase—taking your trained LLM from the lab to the real world. You’ll learn how to wrap your model in APIs, build interactive demos, and choose the best platforms for hosting and scaling your model for wide-reaching use.

Building LLM-powered Apps – Your Own Food Truck

In this module, we will explore how LLMs can power real-world applications, from chatbots to personalized recommendations. You’ll learn how to use no-code tools for rapid prototyping and even build your own fully functional LLM-powered app as a final project.

Keeping it Fresh – Monitoring and Improving

In this module, we will teach you how to ensure your LLM stays fresh and effective over time. You’ll learn how to collect and incorporate feedback, track model performance with monitoring tools, and counteract model drift to keep your application aligned with user needs.

Becoming a Master Chef – Career in LLM Engineering

In this final module, we will help you map out your career path in the rapidly growing field of LLM engineering. You’ll learn how to build a standout portfolio, contribute to open-source projects, and prepare for interviews, ensuring you're ready to take on leadership roles in the industry.