Build a strong mental model of Spring AI architecture and how modern AI applications work end-to-end.
Sub Category
- Data Science
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Objectives
- Build a strong mental model of Spring AI fundamentals for real-world AI application development.
- Understand core AI concepts—LLMs, embeddings, RAG, vectors—through clear visual explanations.
- Master Spring AI architecture and its unified abstractions for modern AI workflows.
- Learn how to design prompts, templates, and structured outputs reliably using Spring AI
- Compare major LLM providers conceptually—OpenAI, Claude, Bedrock, Groq, Vertex, Mistral.
- Understand embedding models, semantic search, retrieval flows, and vector representation.
- Learn conceptual RAG architecture, data preparation, evaluation, and reliability principles.
- Explore vector databases conceptually—Pinecone, Qdrant, Redis, Milvus, PGVector, Cassandra.
- Understand Model Context Protocol (MCP) architecture, lifecycle, and enterprise patterns.
- Learn conceptual observability for AI systems—metrics, drift detection, and failure modes.
- Gain expert clarity on moderation, safety models, governance, and enterprise AI risks.
- Develop expert-level thinking to design, reason, and evaluate Spring AI systems end-to-end.
Pre Requisites
- No prior experience with AI or Spring AI is required.
- A basic understanding of programming is helpful but not mandatory.
- No math, ML background, or prior LLM experience is needed.
- A laptop and willingness to learn are the only requirements.
- This course is designed to remove technical barriers and make AI fundamentals accessible to everyone — beginners included.
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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