Course Outline

Introduction to Open-Source LLMs

  • 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 to proprietary models in the context of government applications

Fine-Tuning and Customizing LLMs for Government Use

  • Data preparation for fine-tuning LLMs to meet specific governmental needs
  • Training and optimizing LLMs using Hugging Face tools and resources
  • Evaluating model performance and implementing strategies for bias mitigation

Building AI Agents with LLMs for Government Applications

  • Introduction to LangChain for developing AI agents tailored for government operations
  • Designing agent-based workflows that integrate LLMs into public sector processes
  • Implementing memory, retrieval-augmented generation (RAG), and action execution in governmental AI systems

Deploying LLM-Based AI Agents for Government Use

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

Security and Compliance in Enterprise AI for Government

  • Ethical considerations and regulatory compliance for government AI systems
  • Mitigating risks associated with AI-driven automation in public sector environments
  • Monitoring and auditing the behavior of AI agents to ensure accountability and transparency

Case Studies and Real-World Applications for Government

  • LLM-powered virtual assistants enhancing citizen services
  • AI-driven document automation improving efficiency in government offices
  • Custom AI agents for advanced enterprise analytics supporting policy-making and decision support

Optimizing and Maintaining LLM-Based Agents for Government Use

  • Continuous model improvement and updating to maintain relevance and accuracy
  • Deploying monitoring and feedback loops to enhance system performance
  • Strategies for cost optimization and performance tuning in government AI systems

Summary and Next Steps for Government Implementation

Requirements

  • Proficient understanding of artificial intelligence and machine learning for government applications
  • Experience with Python programming in a governmental context
  • Knowledge of large language models (LLMs) and natural language processing (NLP) for government use

Audience

  • AI engineers working in the public sector
  • Enterprise software developers supporting government projects
  • Business leaders involved in governmental initiatives
 21 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories