Course Outline

Introduction to Open-Source LLMs for Government

  • Overview of DeepSeek, Mistral, LLaMA, and other open-source models for government use
  • How LLMs work: Transformers, self-attention mechanisms, and training processes
  • Comparing open-source LLMs with proprietary models in a public sector context

Fine-Tuning and Customizing LLMs for Government

  • Data preparation techniques for fine-tuning LLMs to meet specific government needs
  • Training and optimizing LLMs using Hugging Face tools, tailored for public sector applications
  • Evaluating model performance and implementing bias mitigation strategies in government workflows

Building AI Agents with LLMs for Government

  • Introduction to LangChain for developing AI agents suitable for government operations
  • Designing agent-based workflows that align with public sector governance and accountability requirements
  • Implementing memory, retrieval-augmented generation (RAG), and action execution in government contexts

Deploying LLM-Based AI Agents for Government

  • Containerizing AI agents with Docker to ensure compatibility and security in government systems
  • Integrating LLMs into enterprise applications for government agencies
  • Scaling AI agents using cloud services and APIs to support large-scale public sector operations

Security and Compliance in Enterprise AI for Government

  • Ethical considerations and regulatory compliance in the use of AI within government agencies
  • Mitigating risks associated with AI-driven automation in public sector workflows
  • Monitoring and auditing AI agent behavior to ensure transparency and accountability

Case Studies and Real-World Applications for Government

  • LLM-powered virtual assistants for government services
  • AI-driven document automation in public sector operations
  • Custom AI agents for enterprise analytics in government agencies

Optimizing and Maintaining LLM-Based Agents for Government

  • Continuous model improvement and updating to meet evolving government requirements
  • Deploying monitoring and feedback loops to enhance performance in public sector applications
  • Strategies for cost optimization and performance tuning in government AI systems

Summary and Next Steps for Government

Requirements

  • Robust understanding of artificial intelligence and machine learning
  • Experience with Python programming for government applications
  • Familiarity with large language models (LLMs) and natural language processing (NLP)

Audience

  • AI engineers in the public sector
  • Enterprise software developers for government
  • Business leaders in governmental organizations
 21 Hours

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