LLM-Based AI Agents for Enterprise Automation Training Course

Introduction to Open-Source LLMs

• Overview of DeepSeek, Mistral, LLaMA, and other open-source models.
• Mechanisms of LLMs: Transformers, self-attention, and training processes.
• Comparison of open-source LLMs versus proprietary models.

Fine-Tuning and Customizing LLMs

• Data preparation 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.
• Memory management, 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 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 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 updating.
• Deploying monitoring and feedback loops.
• Strategies for cost optimization and performance tuning.

Summary and Next Steps