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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 mechanisms, and training.
- Comparing open-source LLMs against 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 via 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
Requirements
- Solid understanding of Artificial Intelligence and Machine Learning.
- Proficiency in Python programming.
- Familiarity with Large Language Models (LLMs) and Natural Language Processing (NLP).
Audience
- AI Engineers.
- Enterprise Software Developers.
- Business Leaders.
21 Hours
