Build and deploy real AI applications with LLMs, RAG systems, and autonomous agents.
Sub Category
- Data Science
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Objectives
- Understand the foundations of AI engineering, including how Large Language Models (LLMs) work and how they power modern AI applications.
- Build AI applications using LLM APIs, including creating prompts, managing context, and generating structured outputs.
- Design and implement Retrieval Augmented Generation (RAG) systems using embeddings, vector databases, and document retrieval pipelines.
- Develop AI agents capable of multi-step reasoning, tool usage, and workflow automation.
- Deploy AI applications using FastAPI backends and APIs, making them accessible as production-ready services.
- Apply production AI practices such as guardrails, logging, monitoring, and cost optimization to operate AI systems reliably.
Pre Requisites
- Basic familiarity with Python programming is helpful but not strictly required.
- A computer with internet access capable of running development tools and installing Python packages.
- Willingness to learn how AI applications are built and deployed through hands-on exercises.
- No prior experience with AI, machine learning, or LLMs is required — the course covers the fundamentals from the ground up.
- A curiosity about building real AI systems and experimenting with modern AI tools and frameworks.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)