HomeCoursesGrokking the Generative AI System Design
4.3

Intermediate

3h 30min

Updated 1 month ago

Grokking the Generative AI System Design

Explore the design of scalable generative AI systems guided by a structured framework and real-world systems in text, image, audio, and video generation.
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Overview
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This course will prepare you to design generative AI systems with a practical and structured approach. You will begin by exploring the foundational concepts, such as neural networks, transformers, tokenization, embedding, etc. This course introduces a 6-step SCALED framework, a systematic approach to designing robust GenAI systems. Next, through real-world case studies, you will immerse into the design of GenAI systems like text-to-text (e.g., ChatGPT), text-to-image (e.g., Stable Diffusion), text-to-speech (e.g., ElevenLabs), and text-to-video (e.g., SORA). This course describes these systems from a user-focused perspective, emphasizing how user inputs interact with backend processes. Whether you are an ML/software engineer, AI enthusiast, or manager, this course will equip you to design, train, and deploy generative AI models for various use cases. You will gain confidence to approach new challenges in GenAI and leverage advanced techniques to create impactful solutions.
This course will prepare you to design generative AI systems with a practical and structured approach. You will begin by explori...Show More

WHAT YOU'LL LEARN

An understanding of foundational generative AI (GenAI) and distributed machine learning (DML) concepts
An understanding of a 6-step framework (SCALED) to design large-scale GenAI systems
Familiarity with estimating computational resources for training and deploying GenAI systems
The ability to evaluate and improve the performance and accuracy of GenAI models
An understanding of the core services driving real-world GenAI applications
The ability to develop systems for contextual responses and tailored user experiences
Hands-on experience designing robust and scalable architectures to deploy GenAI models for production environments
An understanding of foundational generative AI (GenAI) and distributed machine learning (DML) concepts

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TAKEAWAY SKILLS

Generative AI

System Design

Content

1.

Introduction to GenAI System Design

1 Lessons

Explore generative AI System Design, merging DML, and System Design principles for creating GenAI applications.

2.

Fundamental Concepts in GenAI

5 Lessons

Master foundational concepts, evaluation metrics, and optimization techniques for Generative AI systems.

3.

Back-of-the-envelope Calculations

2 Lessons

Understand back-of-the-envelope calculations for efficiently planning LLM training and deployment.

4.

Systematic Framework for Designing GenAI Systems

1 Lessons

Learn a systematic 6-step framework for designing impactful GenAI systems.

5.

System Design of a Text-to-Text Generation System

2 Lessons

Explore the training and deployment of System Design of an efficient conversational AI system.

6.

System Design of a Text-to-Image Generation System

2 Lessons

Explore the training and deployment of System Design of a robust image generation system.

7.

System Design of a Text-to-Speech Generation System

2 Lessons

Explore the training and deployment of System Design of a realistic speech generation system.

8.

System Design of a Text-to-Video Generation System

2 Lessons

Explore the training and deployment of System Design of a text-to-video generation system.

9.

Conclusion

1 Lessons

Understand key takeaways from the GenAI System Design course and explore the next steps.
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Frequently Asked Questions

What are the key features of a generative AI system?

Key features of generative AI systems include the ability to generate new content, learn patterns in data, and adapt to new information. They can create text, images, music, and even code. Another key feature is their ability to provide real-time responses, which is crucial for interactive applications. This real-time capability is essential for applications like chatbots and live content generation.

What are the common models in generative AI?

Common models include variational autoencoders (VAEs), generative adversarial networks (GANs), large language models like GPT, and diffusion models like Stable Diffusion and SORA. These models use different techniques to generate data. We choose a model based on the use case; for example, GANs are typically used to generate images.

What are examples of generative AI systems?

Examples include text-to-text generation systems like ChatGPT and Gemini, text-to-image generation tools like DALL•E and Midjourney, text-to-speech systems like ElevenLabs, and text-to-video generation systems like Mochi 1 and SORA. These systems showcase the diverse applications of generative AI.

How do I prepare for a generative AI System Design interview?

You can prepare for a GenAI System Design interview by studying the core AI and ML concepts, practicing System Design problems, reviewing common interview questions, and building a strong portfolio of projects. Mock interviews can also be beneficial. You should also learn design frameworks like the SCALED approach to solve unseen problems during the interview.

How do you evaluate the performance of a generative AI system?

We use evaluation metrics (automated and human) to test the performance of GenAI systems. They vary depending on the application. Common methods include measuring accuracy, diversity, fluency, and coherence. Common metrics include BLEU score, CLIP score, ROGUE score, Fréchet inception distance (FID), and mean opinion score (MOS).

What is the difference between generative AI and machine learning?

Generative AI and machine learning are predictive methods but focus on different things. Machine learning makes discriminative predictions, like classifying data, while generative AI makes generative predictions, creating new content. Both learn from data and improve over time, but machine learning focuses on recognizing patterns, while generative AI uses those patterns to generate new data. They represent two powerful branches of AI, each with unique applications and capabilities.

In an interview, how can I demonstrate my understanding of generative AI System Design concepts?

To excel in a generative AI interview, clearly explain your reasoning using examples of case studies. Choose appropriate data and models, like GPT for text, and detail the training process, including techniques like fine-tuning. Finally, outline a robust deployment System Design, showcasing how the model integrates into a real-world system, like a conversational chatbot AI.

What are the best resources for learning about generative AI System Design?

Online courses like “Grokking the Generative AI System Design” provide a solid foundation in the core concepts. Supplement this with research papers and blogs to stay updated on the latest advancements. Then, you should analyze real-world systems like ChatGPT or DALL•E to understand their design choices. Finally, practice designing systems for common generative AI tasks (text-to-text, text-to-image, etc.), exploring different solutions and their tradeoffs to deepen your understanding.