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Course Outline
Introduction to Open-Source LLMs
- Overview of DeepSeek, Mistral, LLaMA, and other open-source models.
- How LLMs function: Transformers, self-attention mechanisms, and training processes.
- Comparing open-source LLMs versus proprietary models.
Fine-Tuning and Customizing LLMs
- Preparing data for fine-tuning.
- Training and optimizing 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.
- Managing memory, retrieval-augmented generation (RAG), and action execution.
Deploying LLM-Based AI Agents
- Containerizing AI agents with Docker.
- Integrating LLMs into enterprise applications.
- Scaling AI agents using cloud services and APIs.
Security and Compliance in Enterprise AI
- Ethical considerations and regulatory compliance.
- Mitigating risks associated with AI-driven automation.
- Monitoring and auditing AI agent behavior.
Case Studies and Real-World Applications
- LLM-powered virtual assistants.
- AI-driven document automation.
- Custom AI agents for enterprise analytics.
Optimizing and Maintaining LLM-Based Agents
- Continuous model improvement and updates.
- Deploying monitoring and feedback loops.
- Strategies for cost optimization and performance tuning.
Summary and Next Steps
Requirements
- A strong grasp of AI and machine learning concepts.
- Practical experience with Python programming.
- Familiarity with large language models (LLMs) and natural language processing (NLP).
Target Audience
- AI engineers.
- Enterprise software developers.
- Business leaders.
21 Hours
