Learning Paths for Technical Professionals
AI Engineering
This starter learning path covers Agentic AI and LLM Engineering, including autonomous agent frameworks (OpenAI SDK, Crew AI, LangGraph, AutoGen), multi-agent orchestration, LLM application development, RAG pipelines, fine-tuning with LoRA/QLoRA, vector databases, multimodal AI, and advanced deployment strategies. Learners gain hands-on experience with leading models (GPT-4, Claude, Gemini, LLAMA), open-source tools (Hugging Face, LangChain), and real-world AI system integration.
Learning objectives:
- Design and implement autonomous AI agents using frameworks such as OpenAI SDK, Crew AI, LangGraph, and AutoGen for real-world applications.
- Develop, evaluate, and deploy LLM-powered applications, including chatbots, RAG systems, and multimodal assistants, leveraging both proprietary and open-source models.
- Master retrieval-augmented generation (RAG) pipelines, vector embeddings, and vector database integration to enhance LLM performance and knowledge retrieval.
- Fine-tune large language models using parameter-efficient techniques like LoRA and QLoRA, and optimize model performance for specific business tasks.
- Build, orchestrate, and deploy multi-agent systems with advanced workflows, persistent memory, structured outputs, and robust UI integrations for production environments
Target audience:
This path is ideal for software engineers, data scientists, and AI practitioners seeking to master agentic AI, LLM engineering, and advanced AI system deployment. It is also suitable for technical professionals aiming to build, fine-tune, and deploy state-of-the-art AI solutions in business or research settings.