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Course Outline
Introduction to Open-Source LLMs
- Overview of DeepSeek, Mistral, LLaMA, and other open-source models
- How LLMs work: Transformers, self-attention, and training
- Comparing open-source LLMs versus proprietary models
Fine-Tuning and Customising LLMs
- Data preparation for fine-tuning
- Training and optimising LLMs using Hugging Face
- Evaluating model performance and mitigating bias
Building AI Agents with LLMs
- Introduction to LangChain for AI agent development
- Designing agent-based workflows with LLMs
- Memory, retrieval-augmented generation (RAG), and action execution
Deploying LLM-Based AI Agents
- Containerising AI agents with Docker
- Integrating LLMs into enterprise applications
- Scaling AI agents with cloud services and APIs
Security and Compliance in Enterprise AI
- Ethical considerations and regulatory compliance
- Mitigating risks in AI-driven automation
- Monitoring and auditing AI agent behaviour
Case Studies and Real-World Applications
- LLM-powered virtual assistants
- AI-driven document automation
- Custom AI agents for enterprise analytics
Optimising and Maintaining LLM-Based Agents
- Continuous model improvement and updating
- Deploying monitoring and feedback loops
- Strategies for cost optimisation and performance tuning
Summary and Next Steps
Requirements
- A strong understanding of AI and machine learning
- Experience with Python programming
- Familiarity with large language models (LLMs) and natural language processing (NLP)
Audience
- AI engineers
- Enterprise software developers
- Business leaders
21 Hours
