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Duration 21 hours
Course Outline
Fundamentals of Open-Source LLMs
- An introduction to DeepSeek, Mistral, LLaMA, and other open-source architectures
- Underlying mechanics of LLMs: Transformers, self-attention mechanisms, and training processes
- Comparative analysis of open-source LLMs versus proprietary systems
Fine-Tuning and Customization of LLMs
- Preparing data sets for effective fine-tuning
- Training and optimizing models through the Hugging Face ecosystem
- Assessing model efficacy and strategies for mitigating bias
Developing AI Agents with LLMs
- Introduction to LangChain for creating autonomous AI agents
- Architecting agent-centric workflows driven by LLMs
- Managing memory, implementing retrieval-augmented generation (RAG), and executing actions
Deployment of LLM-Based AI Agents
- Packaging AI agents using Docker
- Embedding LLM capabilities into existing enterprise platforms
- Scaling agent operations through cloud infrastructure and API integrations
Security and Regulatory Compliance in Enterprise AI
- Navigating ethical frameworks and regulatory standards
- Mitigating potential risks associated with AI-driven automation
- Implementing monitoring and auditing protocols for agent behavior
Case Studies and Practical Applications
- Virtual assistants powered by LLM technology
- AI-assisted document processing and automation
- Specialized AI agents for enterprise data analytics
Optimization and Ongoing Maintenance of LLM-Based Agents
- Strategies for continuous model refinement and updates
- Establishing robust monitoring and feedback mechanisms
- Approaches for cost efficiency and performance tuning
Wrap-Up and Future Directions
Requirements
- Solid grasp of AI and machine learning concepts
- Proficiency in Python programming
- Knowledge of large language models (LLMs) and natural language processing (NLP)
Target Audience
- AI engineers
- Enterprise software developers
- Business executives
