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Course Outline

Fundamental Principles of Open-Source Large Language Models

  • Analysis of DeepSeek, Mistral, LLaMA, and other open-source model frameworks
  • Mechanisms of LLMs: Transformer architectures, self-attention protocols, and training methodologies
  • Comparative assessment of open-source LLMs against proprietary model solutions

Advanced Fine-Tuning and Customization of LLMs

  • Data curation and preparation strategies for fine-tuning
  • Training and optimization of LLMs utilizing the Hugging Face ecosystem
  • Assessment of model efficacy and methods for bias mitigation

Development of AI Agents Utilizing LLMs

  • Application of LangChain for the creation of AI agents for government purposes
  • Architecting agent-based operational workflows with LLMs
  • Integration of memory systems, retrieval-augmented generation (RAG), and action execution capabilities

Deployment of LLM-Based AI Agents in Government Settings

  • Containerization of AI agents via Docker for standardized delivery
  • Integration of LLMs into enterprise-grade and public sector applications
  • Scaling AI agent infrastructure using cloud services and API interfaces

Security and Regulatory Compliance in Enterprise AI

  • Ethical frameworks and adherence to regulatory standards for government and public entities
  • Risk mitigation strategies for AI-driven automation in sensitive environments
  • Continuous monitoring and auditing of AI agent conduct to ensure accountability

Practical Applications and Case Studies

  • Implementation of LLM-powered virtual assistants for citizen and staff support
  • Automation of document processing tasks through AI-driven systems
  • Development of custom AI agents for enterprise and public sector analytics

Optimization and Maintenance of LLM-Based Agents

  • Protocols for continuous model improvement and systematic updating
  • Establishment of monitoring dashboards and feedback mechanisms for governance
  • Strategies for cost efficiency and performance tuning in public sector operations

Summary and Implementation Roadmap

Requirements

  • Proficient understanding of AI and machine learning principles
  • Practical experience with Python programming
  • Working knowledge of large language models (LLMs) and natural language processing (NLP)

Intended Audience

  • AI Engineers
  • Enterprise Software Developers
  • Business and Public Sector Leaders
 21 Hours

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