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Library / Artificial Intelligence

AI System Design Fundamentals for Engineers

On Udemy

About this course

Most engineers can build AI applications.

But building an AI application and designing an AI system are two different skills.

As AI adoption grows, engineers need to understand not only models and APIs, but also how AI applications are structured, how different components work together, and how systems are designed for scalability, reliability, and production environments.

AI System Design Fundamentals for Engineers is designed for developers, software engineers, and AI engineers who want to build a strong foundation in AI system architecture.

The course starts with core system design concepts and gradually introduces production AI architectures through real world case studies.

You will learn how technologies such as LLMs, RAG, AI Agents, Kafka, Redis, Kubernetes, Vector Databases, FastAPI, Databricks, MLOps, and LLMOps can work together inside modern AI systems.

You will also explore architecture patterns used in Machine Learning, Natural Language Processing, Computer Vision, Generative AI, and Agentic AI applications.

This course is especially useful if you already understand basic software development or AI concepts but are new to AI System Design.

If you are already an experienced AI architect or have several years of experience designing large-scale distributed AI systems, some foundational topics may already be familiar to you.

What You Will Learn

Understand the fundamentals of AI System DesignMove from requirements to high level AI system architecture

Understand how Machine Learning systems are structured in production

Learn architecture patterns for Supervised Learning systems

Understand Unsupervised Learning and clustering architectures

Learn how Semi Supervised Learning systems can be designed

Understand Natural Language Processing system architecture

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