Ace your AI engineering interviews with real-world scenarios on RAG, LangChain, Fine-Tuning, and LLM Deployment.
Sub Category
- Other IT & Software
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Objectives
- Evaluate architectural strategies for Retrieval-Augmented Generation (RAG), including Vector DB filtering and Re-ranking models.
- Test your ability to build autonomous LLM Agents using ReAct prompting, Function Calling, and Chain-of-Thought (CoT).
- Assess your proficiency in model alignment, solving catastrophic forgetting, and executing PEFT/QLoRA fine-tuning.
- Validate your MLOps expertise by optimizing LLM deployment with GGUF Quantization, vLLM, and PagedAttention.
Pre Requisites
- A strong foundation in Python and backend software engineering. Familiarity with the concepts of Large Language Models (LLMs) and OpenAI APIs. A desire to pass rigorous technical interviews for specialized "AI Engineer" roles.
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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