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Duration 21 hours
Course Outline
Introduction to Open-Source LLMs
- Survey of DeepSeek, Mistral, LLaMA, and other open-source models
- How LLMs function: Transformers, self-attention, and training mechanisms
- Analysis of open-source LLMs versus proprietary models
Fine-Tuning and Customizing LLMs
- Preparing data for fine-tuning processes
- Training and optimizing LLMs via Hugging Face
- Assessing model performance and addressing bias mitigation
Developing AI Agents with LLMs
- Overview of LangChain for AI agent creation
- Crafting agent-based workflows using LLMs
- Memory, retrieval-augmented generation (RAG), and task execution
Releasing LLM-Based AI Agents
- Containerizing AI agents using Docker
- Embedding LLMs into enterprise applications
- Scaling AI agents through cloud services and APIs
Security and Compliance in Enterprise AI
- Ethical implications and regulatory adherence
- Mitigating risks associated with AI-driven automation
- Supervising and auditing AI agent conduct
Case Studies and Practical Applications
- LLM-driven virtual assistants
- AI-enabled document processing
- Tailored AI agents for enterprise analytics
Enhancing and Sustaining LLM-Based Agents
- Ongoing model refinement and updates
- Implementing monitoring and feedback mechanisms
- Strategies for cost efficiency and performance tuning
Recap and Future Directions
Requirements
- Solid grasp of AI and machine learning principles
- Proficiency in Python programming
- Knowledge of large language models (LLMs) and natural language processing (NLP)
Target Audience
- AI engineers
- Enterprise software developers
- Business leaders
